System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting purchase information of a user from a user device; means for transmitting the collected purchase information to a server; means for receiving and storing the purchase information of the user by the server; means for predicting an optimal reward or coupon based on the purchase information using a generation AI; means for applying the predicted reward or coupon to an account of the user; and means for notifying the user device of an application result of the coupon.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, when users want to use special offers or coupons, they must search for the coupons themselves, enter them, and present them when making a purchase. This is time-consuming, and users often forget to use the special offers or coupons, resulting in missing out on discounts and special offers that they could have received. It is also difficult to determine the optimal way to use special offers and coupons, limiting users' opportunities to enjoy the best purchasing conditions. The purpose of this invention is to solve these problems and enable users to always make purchases under the best conditions without any hassle. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems using the following means. A system is provided that includes a means for collecting user purchase information from a user terminal, a means for transmitting the collected purchase information to a server, a means for the server to receive and store the user purchase information, a means for predicting optimal benefits and coupons based on the purchase information using a generation AI, a means for applying the predicted benefits and coupons to the user's account, and a means for notifying the user terminal of the coupon application results. This allows users to always make purchases using optimal benefits and coupons without any effort. Specifically, the purchase information includes purchase amount, purchased product, purchase date and time, and purchase location information, and by using a generation AI that analyzes the user's purchase history and preference data, optimal benefits and coupons can be automatically predicted and applied.

[0006] A "user terminal" is an electronic device that has the function of collecting purchase information and transmitting it to a server.

[0007] The "server" is a computing device that stores purchase information received from user devices and uses generation AI to optimize rewards and coupons.

[0008] "Purchase information" is data generated when a user purchases a product, and includes the purchase amount, the purchased product, the purchase date and time, and the purchase location.

[0009] "Generative AI" is an artificial intelligence system that analyzes users' purchase history and preference data and predicts optimal benefits and coupons based on that data.

[0010] "Bonus" means any additional benefit or service provided with the purchase of a product.

[0011] A "coupon" is a coupon that users can present at the time of purchase to receive discounts or benefits.

[0012] "Purchase history" is a record of products and services a user has purchased in the past.

[0013] "Preference data" is information that indicates a user's preferences and behavioral patterns.

[0014] "Applying" refers to the process of applying predicted rewards and coupons to a user's account.

[0015] A "notification" is a message that notifies the user that a special offer or coupon has been applied. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention relates to a system that allows users to make purchases using optimal benefits and coupons without hassle. This system is implemented using a user terminal, a server, and a generating AI.

[0038] 1. User Device

[0039] The user device is responsible for collecting purchase information and sending it to the server. When a user purchases a product, the user device acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) and then sends the acquired purchase information to the server.

[0040] 2. Server

[0041] The server receives and stores purchase information sent from the user's device. Based on the stored purchase information, the server uses generative AI to predict the most suitable rewards and coupons. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0042] 3. Generation AI

[0043] The generation AI analyzes the purchase information provided by the server, as well as the user's purchase history and preference data. This allows the AI ​​to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. By combining the purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns it to the server.

[0044] Specific examples

[0045] For example, consider the case where User A makes a purchase of 5,000 yen at a specific shop. User A's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. The server receives this information and stores the purchase information in a database.

[0046] Next, the server passes the saved purchase information to the generation AI and asks it to predict the optimal rewards and coupons. The generation AI analyzes User A's past purchase history and preference data and predicts that a specific coupon would be optimal. For example, a "10% off coupon" or a "1,000 yen off coupon" might be considered.

[0047] The server applies the rewards and coupons to User A's account based on the predictions returned by the generation AI. The server then notifies User A of the application results on his / her device. User A can confirm that the coupon has been applied and use it the next time he / she makes a purchase.

[0048] In this way, the present invention provides a system that allows users to enjoy making purchases under optimal conditions at all times without any hassle.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The user completes the purchase. The user's device collects purchase information (purchase amount, purchased product, purchase date and time, and purchase location information).

[0052] Step 2:

[0053] The user terminal transmits the collected purchase information to the server.

[0054] Step 3:

[0055] The server receives the purchase information sent from the user terminal.

[0056] Step 4:

[0057] The server stores the received purchase information in a database.

[0058] Step 5:

[0059] Based on the stored purchase information, the server asks the generation AI to predict the best perks and coupons.

[0060] Step 6:

[0061] The generation AI analyzes the purchase information provided by the server as well as the user's past purchase history and preference data.

[0062] Step 7:

[0063] Based on the analysis results, the generative AI predicts the best offers and coupons for users.

[0064] Step 8:

[0065] The generation AI returns the prediction results to the server.

[0066] Step 9:

[0067] The server applies rewards and coupons to the user's account based on the predictions returned by the generation AI.

[0068] Step 10:

[0069] The server notifies the user's device of the benefits and coupon application results.

[0070] Step 11:

[0071] The user terminal receives a notification from the server and notifies the user that the coupon has been applied.

[0072] Step 12:

[0073] The user redeems the applied coupon on their next purchase.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] Today's consumers want to efficiently use a wide variety of offers and coupons when purchasing a variety of products and services, but this often requires a great deal of effort. Furthermore, providing optimal offers and coupons in real time based on purchase history and preferences requires advanced analysis, which is difficult to achieve with existing systems. As a result, users may miss out on the best offers, resulting in a poor consumer experience.

[0077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0078] In this invention, the server includes means for collecting user purchase information from the user terminal, means for transmitting the collected purchase information to the server, means for the server to receive and store the user purchase information, means for predicting optimal benefits and coupons based on the purchase information using a generation AI, means for applying the predicted benefits and coupons to the user's account, means for notifying the user terminal of the coupon application results, means for the generation AI to analyze purchase history and preference data in real time, and means for transmitting the purchase information using a secure communication protocol. This makes it possible to automate the optimal application of benefits and coupons and improve the user's consumption experience.

[0079] A "user terminal" is a device used by consumers to collect purchase information and send it to a server, and includes smartphones, tablets, etc.

[0080] "Server" means a computer system that stores and analyzes purchase information received from a user's device and manages the application of special offers and coupons.

[0081] "Purchase information" refers to detailed information when a user purchases a product or service, including the purchase amount, purchased product, purchase date and time, and purchase location information.

[0082] "Generative AI" is an artificial intelligence system that predicts optimal benefits and coupons based on a user's purchase information, purchase history, and preference data.

[0083] "Benefits and coupons" refers to discounts and services provided to users in relation to their purchases, including coupons that provide a partial discount on the purchase price and points.

[0084] "Purchase history" is cumulative data about purchases made by a user in the past.

[0085] "Preference data" is information about a user's preferences and habits, including data on the products and services the user prefers to purchase and the stores they frequently visit.

[0086] A "secure communication protocol" is a set of communication rules for encrypting and sending digital information, maintaining the confidentiality and integrity of the data, such as HTTPS.

[0087] "Real-time" refers to processing or responding to an event within the same time it occurs, meaning that it is applied immediately without delay.

[0088] The present invention relates to a system that allows users to make purchases using optimal benefits and coupons without hassle. The system is implemented using a user terminal, a server, and a generating AI.

[0089] User device details:

[0090] The user device is responsible for collecting purchase information and sending it to the server. Specifically, when a user purchases a product, the user device obtains the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.). The hardware used is typically a smartphone or tablet. The user device then sends the collected information to the server via the internet. To ensure security, it is recommended to use a secure communication protocol such as HTTPS.

[0091] Server details:

[0092] The server receives purchase information sent from the user's device and stores it in a database. Examples of database systems used include MySQL and PostgreSQL. Based on the stored purchase information, the server uses generative AI to predict the most suitable rewards and coupons. Software libraries such as Python and TensorFlow can be used for the generative AI. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0093] More about generative AI:

[0094] The generation AI analyzes the purchase information provided by the server and the user's purchase history and preference data to predict the best offers and coupons for each user. This analysis utilizes machine learning libraries such as Python and TensorFlow. By combining purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns the selection results to the server.

[0095] Examples:

[0096] For example, consider the case where User A makes a purchase of 5,000 yen at a specific shop. User A's device (e.g., a smartphone) collects information about the purchase amount, purchased items, purchase date and time, and purchase location, and sends this information to a server via the Internet. The server receives this information and stores it in a database (e.g., MySQL).

[0097] Next, the server passes the saved purchase information to the generation AI and asks it to predict the most suitable perks and coupons. The generation AI analyzes User A's past purchase history and preference data, and predicts that, for example, a "10% off coupon" or a "1,000 yen off coupon" would be optimal. The server applies this to User A's account and notifies User A's device of the application result. User A can check the notification and use the perks and coupons the next time they make a purchase.

[0098] Example prompt sentence:

[0099] "Please create a prompt for the generative AI to predict the best coupon based on the product and purchase amount purchased by the user."

[0100] "Please provide prompts for the generative AI to suggest the best offers based on your past purchase history and current purchase information."

[0101] In this way, the present invention realizes a system that automatically provides convenient and effective benefits and coupons to users, improving their consumption experience.

[0102] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0103] Step 1: User purchases a product

[0104] A user purchases a product in a store or online shop, selects the product, and completes the purchase process.

[0105] Input: Select product and complete purchase

[0106] Output: Purchase information (purchase amount, purchased item, purchase date and time, purchase location, etc.)

[0107] Step 2: The user device collects purchase information

[0108] After a purchase, the user's device collects purchase information, such as the purchase amount, purchased items, purchase date and time, and purchase location.

[0109] Input: Purchase information (purchase amount, purchased item, purchase date and time, purchase location, etc.)

[0110] Output: Collected purchase information

[0111] Step 3: The user device sends the purchase information to the server

[0112] The user device sends the collected purchase information to a server via the Internet, using a secure communication protocol such as HTTPS to ensure security.

[0113] Input: Collected purchase information

[0114] Output: Purchase information sent to the server

[0115] Step 4: The server receives the purchase information and stores it in a database

[0116] The server receives the purchase information sent from the user's device and stores it in a database, typically using a database system such as MySQL or PostgreSQL.

[0117] Input: Purchase information sent to the server

[0118] Output: Purchase information stored in a database

[0119] Step 5: The server passes the purchase information to the generation AI to predict the best offers and coupons.

[0120] The server passes the purchase information stored in the database to the generation AI, which predicts optimal rewards and coupons based on the purchase information, past purchase history, and preference data.

[0121] Input: Purchase information stored in a database, past purchase history, and preference data

[0122] Output: Predicted best offers and coupons

[0123] Step 6: The generative AI returns the prediction results to the server

[0124] The generation AI returns the results of the data analysis to the server. Specifically, it predicts things like a "10% off coupon" or a "1,000 yen off coupon."

[0125] Input: Best offers and coupon predictions

[0126] Output: Prediction results returned to the server

[0127] Step 7: The server applies rewards or coupons to the user's account based on the predictions.

[0128] The server automatically applies rewards and coupons to the user's account based on the predictions received from the generation AI and updates the database.

[0129] Input: Prediction result returned to the server

[0130] Output: User account with rewards and coupons applied

[0131] Step 8: The server notifies the user device of the benefits and coupon application results.

[0132] The server notifies the user device that the reward or coupon has been applied to the user's account, via push notification, email, or other means.

[0133] Input: User account to which the reward or coupon was applied

[0134] Output: Application results notified to the user's terminal

[0135] Step 9: User checks and redeems offers and coupons

[0136] Users can check the notification sent to their device and use the rewards and coupons on their next purchase. Specifically, they can check the reward details on the smartphone app and present it to receive the discount.

[0137] Input: Application result notified to the user terminal

[0138] Output: Next purchase experience using rewards and coupons

[0139] (Application example 1)

[0140] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0141] Conventional coupon and reward systems make it difficult for users to receive the most appropriate rewards in real time without any effort. This can lead to users overlooking coupons and rewards or having to spend a lot of time and effort to find the best rewards. This problem is particularly pronounced when purchasing in a physical store. If users could receive the most appropriate rewards and coupons in real time while shopping in a physical store, it would improve the user's purchasing experience and increase sales for the store.

[0142] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0143] In this invention, the server includes means for collecting user purchase information from the user terminal, means for transmitting the collected purchase information to the server, means for the server to receive and store the user purchase information, means for predicting optimal benefits and coupons based on the purchase information using a generation AI, means for applying the predicted benefits and coupons to the user's account, means for notifying the user terminal of the coupon application result, and means including a smart device for providing optimal coupons and benefits in real time when the user makes a purchase at a physical store. This allows the user to receive optimal benefits and coupons in real time while shopping at a physical store without any hassle.

[0144] "User terminal" refers to a portable electronic device used by a user, such as a smartphone, smart glasses, or head-mounted display.

[0145] The "means of collection" refers to a function that acquires purchase information (purchase amount, purchased item, purchase date and time, purchase location information, etc.) when a user purchases a product and transmits the data to a server.

[0146] A "server" is a computer system that receives and stores purchase information sent from a user terminal.

[0147] "Generative AI" is artificial intelligence that analyzes users' purchase information, purchase history, and preference data.

[0148] "Means for predicting benefits and coupons" refers to a function that uses generative AI to select the most suitable benefits and coupons for users.

[0149] "Means of applying to a user's account" refers to the function of registering the benefits and coupons predicted by the generation AI to a user's account.

[0150] "Means of notification" refers to a function that notifies the user device that a benefit or coupon has been applied.

[0151] "Real-time delivery" refers to the ability to instantly provide rewards and coupons to users when they make a purchase in a physical store.

[0152] "Smart devices" refer to electronic devices that can be worn or carried by users, and primarily include smartphones, smart glasses, head-mounted displays, etc.

[0153] The present invention is a system that allows users to easily receive optimal rewards and coupons in real time while shopping in a physical store. This system is implemented using a user terminal, a server, and a generation AI.

[0154] 1. User Device

[0155] The user device is a portable electronic device such as a smartphone or smart glasses. When a user purchases an item in a store, the device acquires purchase information (purchase amount, purchased item, purchase date and time, purchase location information) and sends it to the server. This process is performed in real time, allowing the user to receive optimal benefits and coupons while in the store.

[0156] 2. Server

[0157] The server receives purchase information sent from the user's device and stores it in a database. It also provides the purchase information to the generation AI and requests it to predict the best rewards and coupons. The server may use database software such as SQLite or MySQL. Once the best rewards and coupons are predicted, they are automatically applied to the user's account.

[0158] 3. Generation AI

[0159] The Generative AI analyzes the purchase information provided by the server, as well as the user's past purchase history and preference data. This allows it to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. The Generative AI uses advanced artificial intelligence technologies such as the OpenAI API.

[0160] Specific examples

[0161] For example, if a user makes a purchase of 5,000 yen at a specific physical store, the user's device collects this purchase information. The purchase amount, purchased items, purchase date and time, and purchase location information are acquired and sent to the server. The server receives this information and stores it in a database. The server then passes this stored purchase information to the generation AI and requests it to predict the most appropriate perks and coupons. The generation AI analyzes past purchase history and preference data and predicts that, for example, a "10% off coupon" or a "1,000 yen off coupon" would be optimal. The server applies the perks and coupons to the user's account based on the prediction results from the generation AI and notifies the user's device of the application results.

[0162] Example prompts for generative AI models

[0163] User ID: user123

[0164] Purchase information: Purchase amount 5,000 yen, product ["Product A", "Product B"], date and time 2023-10-01 14:00, store "Store X"

[0165] Past purchase history: [Past purchase data]

[0166] Predict the best coupons.”

[0167] This allows users to receive the most suitable offers and coupons in real time while shopping in a physical store without any hassle, improving the user's purchasing experience and also bringing the benefit of increased sales to the store.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] A user purchases a product at a physical store. The user's device (smartphone or smart glasses) collects purchase information in real time. Specifically, it acquires the purchase amount, purchased product, purchase date and time, and purchase location information. The input is the user's detailed purchase information, which the device collects as data.

[0171] Step 2:

[0172] The user terminal sends the collected purchase information to the server. The input is the purchase information collected in step 1, which the terminal prepares as transmission data and sends to the server over the network. The output is the purchase information sent to the server.

[0173] Step 3:

[0174] The server receives the purchase information sent from the user terminal and stores it in a database. The input is the purchase information sent from the user terminal, which the server takes as received data, converts into an appropriate format, and stores in the database. The output is the purchase information stored in the database.

[0175] Step 4:

[0176] The server sends the stored purchase information to the generation AI and requests it to predict the optimal perks and coupons. The input is the purchase information stored in the database, which the server formats for the generation AI and sends. The generation AI performs data analysis based on the input data and predicts the optimal perks and coupons. The output is the predicted perks and coupons.

[0177] Step 5:

[0178] The server applies rewards and coupons to the user's account based on the predictions returned by the generation AI. The input is the predictions returned by the generation AI, which the server processes as data to apply to the user's account. The output is the rewards and coupons applied to the user's account.

[0179] Step 6:

[0180] The server notifies the user terminal of the results of the coupon or reward applied to the user account. The input is the reward or coupon information applied to the user account, which the server prepares as a notification message and sends to the user terminal. The output is the user terminal that received the notification.

[0181] Step 7:

[0182] The user device receives notifications from the server and displays the results of coupon and reward application to the user. The input is the notification message sent from the server, which the device renders to visually present to the user. The output is the notification displayed to the user.

[0183] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0184] The present invention combines a system that allows users to make purchases using optimal benefits and coupons without hassle with an emotion engine that recognizes user emotions. This system is implemented using a user terminal, a server, a generation AI, and an emotion engine.

[0185] 1. User Device

[0186] The user device is responsible for collecting purchase information and sending it to the server. When a user purchases a product, the user device acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.). The acquired purchase information is then sent to the server. The user device also has an interface for recognizing the user's emotions and sends emotional data to the emotion engine.

[0187] 2. Server

[0188] The server receives and stores purchase information and emotion data sent from the user's device. Based on the stored data, the server uses generative AI to predict optimal rewards and coupons. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0189] 3. Generation AI

[0190] The generation AI analyzes the purchase information provided by the server and the user's purchase history and preference data. It also takes into account the emotional data provided by the emotion engine to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. By combining this with the purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns it to the server.

[0191] 4. Emotion Engine

[0192] The emotion engine is a system for recognizing user emotions and analyzes emotion data collected from the user's device. The results of the emotion engine's analysis are provided to the generative AI and used to predict rewards and coupons.

[0193] Specific examples

[0194] For example, consider the case where User B makes a purchase of 5,000 yen at a specific shop. User B's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. Emotional data, such as whether the user was happy with the purchase, is also collected at the same time.

[0195] The server receives this information and emotional data and stores the purchase information in a database. The server then passes the stored data to the generation AI, asking it to predict the optimal rewards and coupons. The generation AI analyzes User B's past purchase history, preference data, and emotional data, and predicts that a specific coupon will be optimal. For example, if the user is happy, it might consider an "additional 10% off coupon," or if the user is sad, it might consider a "1,000 yen off coupon."

[0196] The server applies the rewards and coupons to User B's account based on the predictions returned by the generation AI. The server then notifies User B of the application results. User B can confirm that the coupon has been applied and use it the next time they make a purchase.

[0197] In this way, the present invention provides a system that allows users to enjoy shopping without hassle by using optimal benefits and coupons according to their emotional state.

[0198] The processing flow will be explained below.

[0199] Step 1:

[0200] The user completes the purchase. The user's device collects purchase information (purchase amount, purchased product, purchase date and time, and purchase location information).

[0201] Step 2:

[0202] The user's device collects purchase information along with emotional data from the user, which is acquired through an emotion engine based on the user's facial expressions, tone of voice, etc.

[0203] Step 3:

[0204] The user's device sends the collected purchase information and emotion data to the server.

[0205] Step 4:

[0206] The server receives the purchase information and emotion data sent from the user terminal.

[0207] Step 5:

[0208] The server stores the received purchase information and emotion data in a database.

[0209] Step 6:

[0210] The server provides the stored purchase information and emotional data to the generation AI and asks it to predict the best offers and coupons.

[0211] Step 7:

[0212] The generative AI analyzes purchase information, emotional data, and past purchase history and preference data provided by the server.

[0213] Step 8:

[0214] Based on the analysis results, the generative AI predicts the most appropriate rewards and coupons, taking into account the user's current emotional state. For example, if the user is happy, it will select an "additional 10% off coupon," and if they are sad, it will select a "1,000 yen off coupon."

[0215] Step 9:

[0216] The generation AI returns the prediction results to the server.

[0217] Step 10:

[0218] The server applies rewards and coupons to the user's account based on the predictions returned by the generative AI.

[0219] Step 11:

[0220] The server notifies the user terminal of the results of applying the benefits and coupons.

[0221] Step 12:

[0222] The user terminal receives a notification from the server and notifies the user that the coupon has been applied.

[0223] Step 13:

[0224] The user redeems the applied coupon on their next purchase.

[0225] Example 2

[0226] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0227] Conventional coupon and reward systems only consider users' purchase information, and are therefore unable to provide optimal rewards and coupons that reflect the user's emotional state. This can result in a lack of improvement in the user's purchasing experience and a decrease in satisfaction with the system.

[0228] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0229] In this invention, the server includes means for collecting user purchase information and emotional data from the user terminal, means for receiving and storing the purchase information and emotional data, means for predicting optimal benefits and coupons based on the purchase information and emotional data using a generation AI, means for applying the predicted benefits and coupons to the user's account, and means for notifying the user terminal of the coupon application results, thereby making it possible to provide optimal benefits and coupons that take into account the user's emotional state.

[0230] A "user terminal" is a device used by a user when purchasing a product, and is a device that collects purchase information and emotional data and transmits them to a server.

[0231] "Purchase information" refers to information relating to a transaction that occurs when a user purchases a product, and specifically includes data such as the purchase amount, the purchased product, the purchase date and time, and the purchase location.

[0232] "Emotion data" is data that indicates the user's emotional state, and includes information obtained from the user's facial expressions and voice.

[0233] The "server" is a central processing unit that receives and stores data sent from user devices, and then works with the generation AI to predict optimal benefits and coupons and provide them to users.

[0234] "Generative AI" is an artificial intelligence model that analyzes data stored on a server and predicts optimal rewards and coupons based on users' purchase information and emotional data.

[0235] "Special Offers and Coupons" refers to discounts and other preferential treatments that are applied when a user purchases a product, and are incentives to improve the user's purchasing experience.

[0236] The "means for applying predicted rewards and coupons to a user's account" refers to a system that processes the rewards and coupons predicted by the generating AI to associate them with a user's unique digital account.

[0237] "Means for notifying the user terminal of the results of applying a coupon" refers to a communication method for informing the user terminal that a benefit or coupon has been applied to the user's account, and is a system that has a feedback function to the user.

[0238] This invention is a system that helps users make optimal choices when using special offers and coupons without hassle. It is particularly characterized by combining a user's purchase information with emotional data to provide individually optimized special offers and coupons. This system is realized by combining a user terminal, a server, a generation AI, and an emotion engine.

[0239] 1. User Device

[0240] The user terminal collects information when the user makes a purchase and sends it to the server. It also has an interface for recognizing the user's emotions and sends the emotional data to the emotion engine. This terminal is usually implemented as a device such as a smartphone, tablet, or POS system.

[0241] When a user purchases a product, the user's device acquires purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) and sends this information to the server. The device also collects the user's facial expressions and voice, and sends this as emotion data to the emotion engine. For example, if a user purchases 5,000 yen worth of groceries at a supermarket and is smiling, the emotion data of "joy" is sent along with the purchase amount, purchased product, and location information.

[0242] 2. Server

[0243] The server receives the purchase information and emotion data and stores them in a database. Based on this data, the server generates a prompt message that asks the AI ​​to predict rewards and coupons. For example, a prompt message might be created such as, "User B made a purchase worth 5,000 yen and expressed joy. Please predict the best coupon based on this information."

[0244] The server applies the prediction results (rewards and coupons) returned by the generation AI to the user's account and notifies the user of the results, allowing the user to know which coupons are available for their next purchase.

[0245] 3. Generation AI

[0246] The generation AI analyzes purchase information and sentiment data provided by the server to predict the best rewards and coupons for each user. This analysis also takes into account the user's past purchase history and preference data. The generation AI makes predictions in real time and selects the most effective coupons under specific conditions.

[0247] For example, if the user has a history of making purchases from the same store in the past and expressed the emotion of "joy" at that time, the AI ​​will predict that an "additional 10% off coupon" would be optimal. This prediction result is returned to the server, where it is actually applied.

[0248] 4. Emotion Engine

[0249] The emotion engine is software that recognizes user emotions and analyzes emotional data collected from the user's device. The analysis results are provided to the generation AI, which then reflects the emotional data in predicting rewards and coupons, enabling the provision of coupons that are more optimal for the user.

[0250] The emotion engine analyzes the user's emotional state, such as "joy," "sadness," or "anger," and the generative AI then selects the most appropriate rewards and coupons.

[0251] Specific examples

[0252] When User B makes a purchase of 5,000 yen at a specific shop, User B's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. User emotional data is also collected at the same time.

[0253] The server receives this information and emotion data and stores the purchase information in a database. The server then asks the generation AI to predict rewards and coupons based on the generated prompt. The generation AI analyzes User B's past purchase history, preference data, and emotion data to predict which specific coupon is most appropriate. For example, if the user is happy, an "additional 10% off coupon" is considered, and if the user is sad, a "1,000 yen off coupon" is considered.

[0254] The server applies the rewards and coupons to User B's account based on the predictions returned by the generation AI. The server then notifies User B of the application results on his / her device. User B will receive a notification that the coupon has been applied, and can use it the next time he / she makes a purchase.

[0255] The above is an embodiment of the present invention. This system allows users to enjoy shopping without hassle by using the best offers and coupons according to their emotional state.

[0256] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0257] Step 1:

[0258] The user's device collects purchase information.

[0259] When a user purchases a product, the user's device automatically acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location, etc.). Specifically, this data is collected from POS systems and online purchase screens. The input is the user's purchase action, and the output is the collected purchase information.

[0260] Step 2:

[0261] The user's device collects emotional data.

[0262] The user device uses a built-in camera and microphone to record the user's facial expressions and voice at the time of purchase and analyzes them as emotional data. The input is the user's facial expressions and voice, and the output is the analyzed emotional data.

[0263] Step 3:

[0264] The user terminal transmits the purchase information and emotion data to the server.

[0265] The collected purchase information and emotion data are sent in encrypted form from the user's device to the server. The input is the purchase information and emotion data, and the output is a transmission completion notification to the server.

[0266] Step 4:

[0267] A server stores purchase information and emotion data.

[0268] The server stores the received purchase information and sentiment data in a database, which is important for the generative AI to analyze the data later. The input is the transmitted data, and the output is the stored data.

[0269] Step 5:

[0270] The server generates a prompt message that asks the AI ​​to predict rewards and coupons.

[0271] The server generates a prompt based on the stored data and sends it to the generation AI. For example, the prompt might be something like, "User B made a purchase worth 5,000 yen and expressed happiness. Please predict the best coupon based on this information." The input is the stored data, and the output is the generated prompt.

[0272] Step 6:

[0273] Generative AI analyzes purchase information and sentiment data to predict the best offers and coupons.

[0274] The generative AI reads data from the prompt and predicts rewards and coupons based on the user's past history and preferences. The input is the prompt and related data, and the output is the predicted rewards and coupons.

[0275] Step 7:

[0276] The server applies the generated offers and coupons to the user's account.

[0277] The server applies the coupon to the user's account based on the coupon information returned by the generation AI. The input is the predicted coupon information, and the output is a notification that the coupon has been applied to the user's account.

[0278] Step 8:

[0279] The server notifies the user terminal of the coupon application result.

[0280] The server sends the coupon application result in the form of a notification to the user terminal to inform the user. The input is the coupon application completion notification, and the output is the notification to the user terminal.

[0281] This is the specific processing flow of the program for this system. Through this step, users can receive the best rewards and coupons according to their emotional state.

[0282] (Application example 2)

[0283] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0284] Modern content distribution services face challenges in allowing users to easily receive personalized rewards and coupons. Furthermore, traditional reward and coupon offerings fail to consider the user's emotional state, making it difficult to maximize user satisfaction. Furthermore, there is a demand for more accurate personalization through the integrated use of both viewing data and purchase data. The present invention aims to provide a system that solves these challenges and improves the user experience.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user purchase information and viewing information from the user terminal, means for transmitting the collected purchase information and viewing information to the server, means for the server to receive and store the user purchase information and viewing information, means for predicting optimal benefits and coupons based on the purchase information and viewing information using a generation AI, means for collecting and analyzing user emotion data and applying benefits and coupons based on the emotions, means for applying the predicted benefits and coupons to the user's account, and means for notifying the user terminal of the coupon application results. This makes it possible to provide optimal benefits and coupons that take the user's emotional state into consideration in real time, thereby enhancing personalization for individual users and improving the user experience.

[0286] "User terminal" refers to an electronic device that allows a user to input, view, and operate information, including a smartphone or smart glasses.

[0287] "Purchase information" refers to detailed information about the products purchased by the user, including the purchase amount, the purchased products, the purchase date and time, and the purchase location information.

[0288] "Viewing information" refers to behavioral data of a user when viewing content, and includes the type of content viewed, viewing time, viewing frequency, and the like.

[0289] A "server" is a computing device that receives, stores, and processes data sent by users.

[0290] "Generative AI" is an artificial intelligence system that uses data stored on a server to predict specific outputs, such as optimal rewards or coupons.

[0291] "Emotional data" is data that indicates the emotional state of a user, and is collected using sensors such as a camera and a microphone.

[0292] An "emotion engine" is a system that analyzes collected emotional data and identifies the user's emotional state.

[0293] "Benefits and coupons" are incentives such as discounts and services provided to users, and are applied based on the user's purchasing or viewing behavior.

[0294] An "account" is a user identification database used to manage a user's personal information, behavioral history, benefits, coupons, etc.

[0295] "Notification" is a communication method for conveying information from the system to the user, and is done via a smartphone, smart glasses, etc.

[0296] This invention is a system that collects user viewing and purchasing information from a content distribution service used by the user, and provides optimal benefits and coupons based on this information. This system is implemented by combining a user terminal, a server, a generation AI, and an emotion engine.

[0297] First, the user terminal is an electronic device used by the user, such as a smartphone or smart glasses. When the user watches video or music content, viewing information (type of content watched, viewing time, viewing frequency, etc.) and purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) are collected. In addition, emotion data is collected using the user's emotion recognition function. This information is sent to the server.

[0298] The server receives and stores viewing information, purchase information, and emotional data sent from the user's device. This information is stored in a database on the server. Based on this stored data, the server uses generative AI to predict optimal rewards and coupons. Rewards and coupons are predicted through an integrated analysis of the user's purchase history, preference data, and emotional data.

[0299] Generative AI models such as OpenAI's GPT series are used. This generative AI analyzes viewing information, purchase information, and emotional data to predict the best rewards and coupons for users in real time. For example, incentives are provided based on the user's emotions at the time, such as a "reward for the next viewing being free" when the user is watching an enjoyable video, or a "reward for viewing points" when the user is sad.

[0300] The emotion engine analyzes emotion data acquired from the user's device and identifies the user's emotional state. For example, emotion recognition software such as the Affectiva SDK is used. The emotion data analyzed by the emotion engine is provided to the generative AI.

[0301] When a reward or coupon is predicted, the server applies it to the user's account and sends a notification to the user's device. The user's device then notifies the user of the details of the reward or coupon that has been applied, allowing the user to easily confirm that the reward or coupon has been applied.

[0302] Specific examples

[0303] Suppose a user is using a social networking app on their smartphone. While the user is watching a funny comedy show, the smartphone's camera and microphone detect the emotion "happy / laughing." This information is sent to a server, where a generative AI predicts rewards based on the following prompt:

[0304] Viewing data: {Video title: 'Funny comedy show', Viewing time: '30 minutes'}

[0305] Emotion data: {Happiness: 80%, Laughter: 70%}

[0306] User history: {Past month viewing time: '30 hours', Viewing history: 'Comedy, Action'}

[0307] Please suggest the best offer.

[0308] Based on this prompt, the AI ​​generator predicts the "next viewing free offer" and applies it to the user's account. This offer is then notified to the user's device, allowing them to use it the next time they watch.

[0309] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0310] Step 1:

[0311] The user device begins collecting the user's viewing information and emotional data. Specifically, it uses the camera and microphone installed in the smartphone or smart glasses to collect the user's viewing behavior (how much of what content was viewed) and emotional state (happiness, sadness, excitement, etc.) in real time. This data is then temporarily stored.

[0312] Input: Viewing information (type of content, viewing time, viewing frequency), emotional data (video and audio data from camera and microphone)

[0313] Output: Collected viewing information and emotional data

[0314] Step 2:

[0315] The user device transmits the collected viewing information and emotion data to the server, which receives this data and stores it in a database.

[0316] Input: Collected viewing information and emotional data

[0317] Output: Data saved on the server

[0318] Step 3:

[0319] The server inputs prompts into the generative AI model based on the stored viewing information and emotional data to predict optimal rewards and coupons. The generative AI analyzes viewing information, emotional data, and the user's past viewing history and preference data to generate rewards and coupons.

[0320] Input: Viewing information, emotional data, user's past viewing history and preference data

[0321] Output: Generated offers and coupons

[0322] Step 4:

[0323] The server receives the results from the generative AI and applies the best offers and coupons to the user's account, which are then stored in the user's account database.

[0324] Input: Rewards and coupons from generated AI

[0325] Output: Rewards and coupons applied to user account

[0326] Step 5:

[0327] The server notifies the user device of the applied benefits and coupons. The user device receives this notification and displays the details of the benefits and coupons to the user. The user can check the notification and use these benefits and coupons the next time they watch or purchase.

[0328] Input: Application result to user account

[0329] Output: Notification of special offers and coupons to user devices

[0330] This will enable a system that delivers highly personalized offers and coupons in real time based on the user's emotional state and viewing information.

[0331] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0332] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0333] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0334] [Second embodiment]

[0335] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0336] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0337] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0338] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0339] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0340] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0341] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0342] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0343] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0344] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0345] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0346] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0347] The present invention relates to a system that allows users to make purchases using optimal benefits and coupons without hassle. This system is implemented using a user terminal, a server, and a generating AI.

[0348] 1. User Device

[0349] The user device is responsible for collecting purchase information and sending it to the server. When a user purchases a product, the user device acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) and then sends the acquired purchase information to the server.

[0350] 2. Server

[0351] The server receives and stores purchase information sent from the user's device. Based on the stored purchase information, the server uses generative AI to predict the most suitable rewards and coupons. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0352] 3. Generation AI

[0353] The generation AI analyzes the purchase information provided by the server, as well as the user's purchase history and preference data. This allows the AI ​​to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. By combining the purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns it to the server.

[0354] Specific examples

[0355] For example, consider the case where User A makes a purchase of 5,000 yen at a specific shop. User A's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. The server receives this information and stores the purchase information in a database.

[0356] Next, the server passes the saved purchase information to the generation AI and asks it to predict the optimal rewards and coupons. The generation AI analyzes User A's past purchase history and preference data and predicts that a specific coupon would be optimal. For example, a "10% off coupon" or a "1,000 yen off coupon" might be considered.

[0357] The server applies the rewards and coupons to User A's account based on the predictions returned by the generation AI. The server then notifies User A of the application results on his / her device. User A can confirm that the coupon has been applied and use it the next time he / she makes a purchase.

[0358] In this way, the present invention provides a system that allows users to enjoy making purchases under optimal conditions at all times without any hassle.

[0359] The processing flow will be explained below.

[0360] Step 1:

[0361] The user completes the purchase. The user's device collects purchase information (purchase amount, purchased product, purchase date and time, and purchase location information).

[0362] Step 2:

[0363] The user terminal transmits the collected purchase information to the server.

[0364] Step 3:

[0365] The server receives the purchase information sent from the user terminal.

[0366] Step 4:

[0367] The server stores the received purchase information in a database.

[0368] Step 5:

[0369] Based on the stored purchase information, the server asks the generation AI to predict the best perks and coupons.

[0370] Step 6:

[0371] The generation AI analyzes the purchase information provided by the server as well as the user's past purchase history and preference data.

[0372] Step 7:

[0373] Based on the analysis results, the generative AI predicts the best offers and coupons for users.

[0374] Step 8:

[0375] The generation AI returns the prediction results to the server.

[0376] Step 9:

[0377] The server applies rewards and coupons to the user's account based on the predictions returned by the generation AI.

[0378] Step 10:

[0379] The server notifies the user's device of the benefits and coupon application results.

[0380] Step 11:

[0381] The user terminal receives a notification from the server and notifies the user that the coupon has been applied.

[0382] Step 12:

[0383] The user redeems the applied coupon on their next purchase.

[0384] Example 1

[0385] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0386] Today's consumers want to efficiently use a wide variety of offers and coupons when purchasing a variety of products and services, but this often requires a great deal of effort. Furthermore, providing optimal offers and coupons in real time based on purchase history and preferences requires advanced analysis, which is difficult to achieve with existing systems. As a result, users may miss out on the best offers, resulting in a poor consumer experience.

[0387] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0388] In this invention, the server includes means for collecting user purchase information from the user terminal, means for transmitting the collected purchase information to the server, means for the server to receive and store the user purchase information, means for predicting optimal benefits and coupons based on the purchase information using a generation AI, means for applying the predicted benefits and coupons to the user's account, means for notifying the user terminal of the coupon application results, means for the generation AI to analyze purchase history and preference data in real time, and means for transmitting the purchase information using a secure communication protocol. This makes it possible to automate the optimal application of benefits and coupons and improve the user's consumption experience.

[0389] A "user terminal" is a device used by consumers to collect purchase information and send it to a server, and includes smartphones, tablets, etc.

[0390] "Server" means a computer system that stores and analyzes purchase information received from a user's device and manages the application of special offers and coupons.

[0391] "Purchase information" refers to detailed information when a user purchases a product or service, including the purchase amount, purchased product, purchase date and time, and purchase location information.

[0392] "Generative AI" is an artificial intelligence system that predicts optimal benefits and coupons based on a user's purchase information, purchase history, and preference data.

[0393] "Benefits and coupons" refers to discounts and services provided to users in relation to their purchases, including coupons that provide a partial discount on the purchase price and points.

[0394] "Purchase history" is cumulative data about purchases made by a user in the past.

[0395] "Preference data" is information about a user's preferences and habits, including data on the products and services the user prefers to purchase and the stores they frequently visit.

[0396] A "secure communication protocol" is a set of communication rules for encrypting and sending digital information, maintaining the confidentiality and integrity of the data, such as HTTPS.

[0397] "Real-time" refers to processing or responding to an event within the same time it occurs, meaning that it is applied immediately without delay.

[0398] The present invention relates to a system that allows users to make purchases using optimal benefits and coupons without hassle. The system is implemented using a user terminal, a server, and a generating AI.

[0399] User device details:

[0400] The user device is responsible for collecting purchase information and sending it to the server. Specifically, when a user purchases a product, the user device obtains the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.). The hardware used is typically a smartphone or tablet. The user device then sends the collected information to the server via the internet. To ensure security, it is recommended to use a secure communication protocol such as HTTPS.

[0401] Server details:

[0402] The server receives purchase information sent from the user's device and stores it in a database. Examples of database systems used include MySQL and PostgreSQL. Based on the stored purchase information, the server uses generative AI to predict the most suitable rewards and coupons. Software libraries such as Python and TensorFlow can be used for the generative AI. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0403] More about generative AI:

[0404] The generation AI analyzes the purchase information provided by the server and the user's purchase history and preference data to predict the best offers and coupons for each user. This analysis utilizes machine learning libraries such as Python and TensorFlow. By combining purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns the selection results to the server.

[0405] Examples:

[0406] For example, consider the case where User A makes a purchase of 5,000 yen at a specific shop. User A's device (e.g., a smartphone) collects information about the purchase amount, purchased items, purchase date and time, and purchase location, and sends this information to a server via the Internet. The server receives this information and stores it in a database (e.g., MySQL).

[0407] Next, the server passes the saved purchase information to the generation AI and asks it to predict the most suitable perks and coupons. The generation AI analyzes User A's past purchase history and preference data, and predicts that, for example, a "10% off coupon" or a "1,000 yen off coupon" would be optimal. The server applies this to User A's account and notifies User A's device of the application result. User A can check the notification and use the perks and coupons the next time they make a purchase.

[0408] Example prompt sentence:

[0409] "Please create a prompt for the generative AI to predict the best coupon based on the product and purchase amount purchased by the user."

[0410] "Please provide prompts for the generative AI to suggest the best offers based on your past purchase history and current purchase information."

[0411] In this way, the present invention realizes a system that automatically provides convenient and effective benefits and coupons to users, improving their consumption experience.

[0412] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0413] Step 1: User purchases a product

[0414] A user purchases a product in a store or online shop, selects the product, and completes the purchase process.

[0415] Input: Select product and complete purchase

[0416] Output: Purchase information (purchase amount, purchased item, purchase date and time, purchase location, etc.)

[0417] Step 2: The user device collects purchase information

[0418] After a purchase, the user's device collects purchase information, such as the purchase amount, purchased items, purchase date and time, and purchase location.

[0419] Input: Purchase information (purchase amount, purchased item, purchase date and time, purchase location, etc.)

[0420] Output: Collected purchase information

[0421] Step 3: The user device sends the purchase information to the server

[0422] The user device sends the collected purchase information to a server via the Internet, using a secure communication protocol such as HTTPS to ensure security.

[0423] Input: Collected purchase information

[0424] Output: Purchase information sent to the server

[0425] Step 4: The server receives the purchase information and stores it in a database

[0426] The server receives the purchase information sent from the user's device and stores it in a database, typically using a database system such as MySQL or PostgreSQL.

[0427] Input: Purchase information sent to the server

[0428] Output: Purchase information stored in a database

[0429] Step 5: The server passes the purchase information to the generation AI to predict the best offers and coupons.

[0430] The server passes the purchase information stored in the database to the generation AI, which predicts optimal rewards and coupons based on the purchase information, past purchase history, and preference data.

[0431] Input: Purchase information stored in a database, past purchase history, and preference data

[0432] Output: Predicted best offers and coupons

[0433] Step 6: The generative AI returns the prediction results to the server

[0434] The generation AI returns the results of the data analysis to the server. Specifically, it predicts things like a "10% off coupon" or a "1,000 yen off coupon."

[0435] Input: Best offers and coupon predictions

[0436] Output: Prediction results returned to the server

[0437] Step 7: The server applies rewards or coupons to the user's account based on the predictions.

[0438] The server automatically applies rewards and coupons to the user's account based on the predictions received from the generation AI and updates the database.

[0439] Input: Prediction result returned to the server

[0440] Output: User account with rewards and coupons applied

[0441] Step 8: The server notifies the user device of the benefits and coupon application results.

[0442] The server notifies the user device that the reward or coupon has been applied to the user's account, via push notification, email, or other means.

[0443] Input: User account to which the reward or coupon was applied

[0444] Output: Application results notified to the user's terminal

[0445] Step 9: User checks and redeems offers and coupons

[0446] Users can check the notification sent to their device and use the rewards and coupons on their next purchase. Specifically, they can check the reward details on the smartphone app and present it to receive the discount.

[0447] Input: Application result notified to the user terminal

[0448] Output: Next purchase experience using rewards and coupons

[0449] (Application example 1)

[0450] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0451] Conventional coupon and reward systems make it difficult for users to receive the most appropriate rewards in real time without any effort. This can lead to users overlooking coupons and rewards or having to spend a lot of time and effort to find the best rewards. This problem is particularly pronounced when purchasing in a physical store. If users could receive the most appropriate rewards and coupons in real time while shopping in a physical store, it would improve the user's purchasing experience and increase sales for the store.

[0452] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0453] In this invention, the server includes means for collecting user purchase information from the user terminal, means for transmitting the collected purchase information to the server, means for the server to receive and store the user purchase information, means for predicting optimal benefits and coupons based on the purchase information using a generation AI, means for applying the predicted benefits and coupons to the user's account, means for notifying the user terminal of the coupon application result, and means including a smart device for providing optimal coupons and benefits in real time when the user makes a purchase at a physical store. This allows the user to receive optimal benefits and coupons in real time while shopping at a physical store without any hassle.

[0454] "User terminal" refers to a portable electronic device used by a user, such as a smartphone, smart glasses, or head-mounted display.

[0455] The "means of collection" refers to a function that acquires purchase information (purchase amount, purchased item, purchase date and time, purchase location information, etc.) when a user purchases a product and transmits the data to a server.

[0456] A "server" is a computer system that receives and stores purchase information sent from a user terminal.

[0457] "Generative AI" is artificial intelligence that analyzes users' purchase information, purchase history, and preference data.

[0458] "Means for predicting benefits and coupons" refers to a function that uses generative AI to select the most suitable benefits and coupons for users.

[0459] "Means of applying to a user's account" refers to the function of registering the benefits and coupons predicted by the generation AI to a user's account.

[0460] "Means of notification" refers to a function that notifies the user device that a benefit or coupon has been applied.

[0461] "Real-time delivery" refers to the ability to instantly provide rewards and coupons to users when they make a purchase in a physical store.

[0462] "Smart devices" refer to electronic devices that can be worn or carried by users, and primarily include smartphones, smart glasses, head-mounted displays, etc.

[0463] The present invention is a system that allows users to easily receive optimal rewards and coupons in real time while shopping in a physical store. This system is implemented using a user terminal, a server, and a generation AI.

[0464] 1. User Device

[0465] The user device is a portable electronic device such as a smartphone or smart glasses. When a user purchases an item in a store, the device acquires purchase information (purchase amount, purchased item, purchase date and time, purchase location information) and sends it to the server. This process is performed in real time, allowing the user to receive optimal benefits and coupons while in the store.

[0466] 2. Server

[0467] The server receives purchase information sent from the user's device and stores it in a database. It also provides the purchase information to the generation AI and requests it to predict the best rewards and coupons. The server may use database software such as SQLite or MySQL. Once the best rewards and coupons are predicted, they are automatically applied to the user's account.

[0468] 3. Generation AI

[0469] The Generative AI analyzes the purchase information provided by the server, as well as the user's past purchase history and preference data. This allows it to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. The Generative AI uses advanced artificial intelligence technologies such as the OpenAI API.

[0470] Specific examples

[0471] For example, if a user makes a purchase of 5,000 yen at a specific physical store, the user's device collects this purchase information. The purchase amount, purchased items, purchase date and time, and purchase location information are acquired and sent to the server. The server receives this information and stores it in a database. The server then passes this stored purchase information to the generation AI and requests it to predict the most appropriate perks and coupons. The generation AI analyzes past purchase history and preference data and predicts that, for example, a "10% off coupon" or a "1,000 yen off coupon" would be optimal. The server applies the perks and coupons to the user's account based on the prediction results from the generation AI and notifies the user's device of the application results.

[0472] Example prompts for generative AI models

[0473] User ID: user123

[0474] Purchase information: Purchase amount 5,000 yen, product ["Product A", "Product B"], date and time 2023-10-01 14:00, store "Store X"

[0475] Past purchase history: [Past purchase data]

[0476] Predict the best coupons.”

[0477] This allows users to receive the most suitable offers and coupons in real time while shopping in a physical store without any hassle, improving the user's purchasing experience and also bringing the benefit of increased sales to the store.

[0478] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0479] Step 1:

[0480] A user purchases a product at a physical store. The user's device (smartphone or smart glasses) collects purchase information in real time. Specifically, it acquires the purchase amount, purchased product, purchase date and time, and purchase location information. The input is the user's detailed purchase information, which the device collects as data.

[0481] Step 2:

[0482] The user terminal sends the collected purchase information to the server. The input is the purchase information collected in step 1, which the terminal prepares as transmission data and sends to the server over the network. The output is the purchase information sent to the server.

[0483] Step 3:

[0484] The server receives the purchase information sent from the user terminal and stores it in a database. The input is the purchase information sent from the user terminal, which the server takes as received data, converts into an appropriate format, and stores in the database. The output is the purchase information stored in the database.

[0485] Step 4:

[0486] The server sends the stored purchase information to the generation AI and requests it to predict the optimal perks and coupons. The input is the purchase information stored in the database, which the server formats for the generation AI and sends. The generation AI performs data analysis based on the input data and predicts the optimal perks and coupons. The output is the predicted perks and coupons.

[0487] Step 5:

[0488] The server applies rewards and coupons to the user's account based on the predictions returned by the generation AI. The input is the predictions returned by the generation AI, which the server processes as data to apply to the user's account. The output is the rewards and coupons applied to the user's account.

[0489] Step 6:

[0490] The server notifies the user terminal of the results of the coupon or reward applied to the user account. The input is the reward or coupon information applied to the user account, which the server prepares as a notification message and sends to the user terminal. The output is the user terminal that received the notification.

[0491] Step 7:

[0492] The user device receives notifications from the server and displays the results of coupon and reward application to the user. The input is the notification message sent from the server, which the device renders to visually present to the user. The output is the notification displayed to the user.

[0493] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0494] The present invention combines a system that allows users to make purchases using optimal benefits and coupons without hassle with an emotion engine that recognizes user emotions. This system is implemented using a user terminal, a server, a generation AI, and an emotion engine.

[0495] 1. User Device

[0496] The user device is responsible for collecting purchase information and sending it to the server. When a user purchases a product, the user device acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.). The acquired purchase information is then sent to the server. The user device also has an interface for recognizing the user's emotions and sends emotional data to the emotion engine.

[0497] 2. Server

[0498] The server receives and stores purchase information and emotion data sent from the user's device. Based on the stored data, the server uses generative AI to predict optimal rewards and coupons. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0499] 3. Generation AI

[0500] The generation AI analyzes the purchase information provided by the server and the user's purchase history and preference data. It also takes into account the emotional data provided by the emotion engine to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. By combining this with the purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns it to the server.

[0501] 4. Emotion Engine

[0502] The emotion engine is a system for recognizing user emotions and analyzes emotion data collected from the user's device. The results of the emotion engine's analysis are provided to the generative AI and used to predict rewards and coupons.

[0503] Specific examples

[0504] For example, consider the case where User B makes a purchase of 5,000 yen at a specific shop. User B's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. Emotional data, such as whether the user was happy with the purchase, is also collected at the same time.

[0505] The server receives this information and emotional data and stores the purchase information in a database. The server then passes the stored data to the generation AI, asking it to predict the optimal rewards and coupons. The generation AI analyzes User B's past purchase history, preference data, and emotional data, and predicts that a specific coupon will be optimal. For example, if the user is happy, it might consider an "additional 10% off coupon," or if the user is sad, it might consider a "1,000 yen off coupon."

[0506] The server applies the rewards and coupons to User B's account based on the predictions returned by the generation AI. The server then notifies User B of the application results. User B can confirm that the coupon has been applied and use it the next time they make a purchase.

[0507] In this way, the present invention provides a system that allows users to enjoy shopping without hassle by using optimal benefits and coupons according to their emotional state.

[0508] The processing flow will be explained below.

[0509] Step 1:

[0510] The user completes the purchase. The user's device collects purchase information (purchase amount, purchased product, purchase date and time, and purchase location information).

[0511] Step 2:

[0512] The user's device collects purchase information along with emotional data from the user, which is acquired through an emotion engine based on the user's facial expressions, tone of voice, etc.

[0513] Step 3:

[0514] The user's device sends the collected purchase information and emotion data to the server.

[0515] Step 4:

[0516] The server receives the purchase information and emotion data sent from the user terminal.

[0517] Step 5:

[0518] The server stores the received purchase information and emotion data in a database.

[0519] Step 6:

[0520] The server provides the stored purchase information and emotional data to the generation AI and asks it to predict the best offers and coupons.

[0521] Step 7:

[0522] The generative AI analyzes purchase information, emotional data, and past purchase history and preference data provided by the server.

[0523] Step 8:

[0524] Based on the analysis results, the generative AI predicts the most appropriate rewards and coupons, taking into account the user's current emotional state. For example, if the user is happy, it will select an "additional 10% off coupon," and if they are sad, it will select a "1,000 yen off coupon."

[0525] Step 9:

[0526] The generation AI returns the prediction results to the server.

[0527] Step 10:

[0528] The server applies rewards and coupons to the user's account based on the predictions returned by the generative AI.

[0529] Step 11:

[0530] The server notifies the user terminal of the results of applying the benefits and coupons.

[0531] Step 12:

[0532] The user terminal receives a notification from the server and notifies the user that the coupon has been applied.

[0533] Step 13:

[0534] The user redeems the applied coupon on their next purchase.

[0535] Example 2

[0536] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0537] Conventional coupon and reward systems only consider users' purchase information, and are therefore unable to provide optimal rewards and coupons that reflect the user's emotional state. This can result in a lack of improvement in the user's purchasing experience and a decrease in satisfaction with the system.

[0538] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0539] In this invention, the server includes means for collecting user purchase information and emotional data from the user terminal, means for receiving and storing the purchase information and emotional data, means for predicting optimal benefits and coupons based on the purchase information and emotional data using a generation AI, means for applying the predicted benefits and coupons to the user's account, and means for notifying the user terminal of the coupon application results, thereby making it possible to provide optimal benefits and coupons that take into account the user's emotional state.

[0540] A "user terminal" is a device used by a user when purchasing a product, and is a device that collects purchase information and emotional data and transmits them to a server.

[0541] "Purchase information" refers to information relating to a transaction that occurs when a user purchases a product, and specifically includes data such as the purchase amount, the purchased product, the purchase date and time, and the purchase location.

[0542] "Emotion data" is data that indicates the user's emotional state, and includes information obtained from the user's facial expressions and voice.

[0543] The "server" is a central processing unit that receives and stores data sent from user devices, and then works with the generation AI to predict optimal benefits and coupons and provide them to users.

[0544] "Generative AI" is an artificial intelligence model that analyzes data stored on a server and predicts optimal rewards and coupons based on users' purchase information and emotional data.

[0545] "Special Offers and Coupons" refers to discounts and other preferential treatments that are applied when a user purchases a product, and are incentives to improve the user's purchasing experience.

[0546] The "means for applying predicted rewards and coupons to a user's account" refers to a system that processes the rewards and coupons predicted by the generating AI to associate them with a user's unique digital account.

[0547] "Means for notifying the user terminal of the results of applying a coupon" refers to a communication method for informing the user terminal that a benefit or coupon has been applied to the user's account, and is a system that has a feedback function to the user.

[0548] This invention is a system that helps users make optimal choices when using special offers and coupons without hassle. It is particularly characterized by combining a user's purchase information with emotional data to provide individually optimized special offers and coupons. This system is realized by combining a user terminal, a server, a generation AI, and an emotion engine.

[0549] 1. User Device

[0550] The user terminal collects information when the user makes a purchase and sends it to the server. It also has an interface for recognizing the user's emotions and sends the emotional data to the emotion engine. This terminal is usually implemented as a device such as a smartphone, tablet, or POS system.

[0551] When a user purchases a product, the user's device acquires purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) and sends this information to the server. The device also collects the user's facial expressions and voice, and sends this as emotion data to the emotion engine. For example, if a user purchases 5,000 yen worth of groceries at a supermarket and is smiling, the emotion data of "joy" is sent along with the purchase amount, purchased product, and location information.

[0552] 2. Server

[0553] The server receives the purchase information and emotion data and stores them in a database. Based on this data, the server generates a prompt message that asks the AI ​​to predict rewards and coupons. For example, a prompt message might be created such as, "User B made a purchase worth 5,000 yen and expressed joy. Please predict the best coupon based on this information."

[0554] The server applies the prediction results (rewards and coupons) returned by the generation AI to the user's account and notifies the user of the results, allowing the user to know which coupons are available for their next purchase.

[0555] 3. Generation AI

[0556] The generation AI analyzes purchase information and sentiment data provided by the server to predict the best rewards and coupons for each user. This analysis also takes into account the user's past purchase history and preference data. The generation AI makes predictions in real time and selects the most effective coupons under specific conditions.

[0557] For example, if the user has a history of making purchases from the same store in the past and expressed the emotion of "joy" at that time, the AI ​​will predict that an "additional 10% off coupon" would be optimal. This prediction result is returned to the server, where it is actually applied.

[0558] 4. Emotion Engine

[0559] The emotion engine is software that recognizes user emotions and analyzes emotional data collected from the user's device. The analysis results are provided to the generation AI, which then reflects the emotional data in predicting rewards and coupons, enabling the provision of coupons that are more optimal for the user.

[0560] The emotion engine analyzes the user's emotional state, such as "joy," "sadness," or "anger," and the generative AI then selects the most appropriate rewards and coupons.

[0561] Specific examples

[0562] When User B makes a purchase of 5,000 yen at a specific shop, User B's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. User emotional data is also collected at the same time.

[0563] The server receives this information and emotion data and stores the purchase information in a database. The server then asks the generation AI to predict rewards and coupons based on the generated prompt. The generation AI analyzes User B's past purchase history, preference data, and emotion data to predict which specific coupon is most appropriate. For example, if the user is happy, an "additional 10% off coupon" is considered, and if the user is sad, a "1,000 yen off coupon" is considered.

[0564] The server applies the rewards and coupons to User B's account based on the predictions returned by the generation AI. The server then notifies User B of the application results on his / her device. User B will receive a notification that the coupon has been applied, and can use it the next time he / she makes a purchase.

[0565] The above is an embodiment of the present invention. This system allows users to enjoy shopping without hassle by using the best offers and coupons according to their emotional state.

[0566] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0567] Step 1:

[0568] The user's device collects purchase information.

[0569] When a user purchases a product, the user's device automatically acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location, etc.). Specifically, this data is collected from POS systems and online purchase screens. The input is the user's purchase action, and the output is the collected purchase information.

[0570] Step 2:

[0571] The user's device collects emotional data.

[0572] The user device uses a built-in camera and microphone to record the user's facial expressions and voice at the time of purchase and analyzes them as emotional data. The input is the user's facial expressions and voice, and the output is the analyzed emotional data.

[0573] Step 3:

[0574] The user terminal transmits the purchase information and emotion data to the server.

[0575] The collected purchase information and emotion data are sent in encrypted form from the user's device to the server. The input is the purchase information and emotion data, and the output is a transmission completion notification to the server.

[0576] Step 4:

[0577] A server stores purchase information and emotion data.

[0578] The server stores the received purchase information and sentiment data in a database, which is important for the generative AI to analyze the data later. The input is the transmitted data, and the output is the stored data.

[0579] Step 5:

[0580] The server generates a prompt message that asks the AI ​​to predict rewards and coupons.

[0581] The server generates a prompt based on the stored data and sends it to the generation AI. For example, the prompt might be something like, "User B made a purchase worth 5,000 yen and expressed happiness. Please predict the best coupon based on this information." The input is the stored data, and the output is the generated prompt.

[0582] Step 6:

[0583] Generative AI analyzes purchase information and sentiment data to predict the best offers and coupons.

[0584] The generative AI reads data from the prompt and predicts rewards and coupons based on the user's past history and preferences. The input is the prompt and related data, and the output is the predicted rewards and coupons.

[0585] Step 7:

[0586] The server applies the generated offers and coupons to the user's account.

[0587] The server applies the coupon to the user's account based on the coupon information returned by the generation AI. The input is the predicted coupon information, and the output is a notification that the coupon has been applied to the user's account.

[0588] Step 8:

[0589] The server notifies the user terminal of the coupon application result.

[0590] The server sends the coupon application result in the form of a notification to the user terminal to inform the user. The input is the coupon application completion notification, and the output is the notification to the user terminal.

[0591] This is the specific processing flow of the program for this system. Through this step, users can receive the best rewards and coupons according to their emotional state.

[0592] (Application example 2)

[0593] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0594] Modern content distribution services face challenges in allowing users to easily receive personalized rewards and coupons. Furthermore, traditional reward and coupon offerings fail to consider the user's emotional state, making it difficult to maximize user satisfaction. Furthermore, there is a demand for more accurate personalization through the integrated use of both viewing data and purchase data. The present invention aims to provide a system that solves these challenges and improves the user experience.

[0595] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user purchase information and viewing information from the user terminal, means for transmitting the collected purchase information and viewing information to the server, means for the server to receive and store the user purchase information and viewing information, means for predicting optimal benefits and coupons based on the purchase information and viewing information using a generation AI, means for collecting and analyzing user emotion data and applying benefits and coupons based on the emotions, means for applying the predicted benefits and coupons to the user's account, and means for notifying the user terminal of the coupon application results. This makes it possible to provide optimal benefits and coupons that take the user's emotional state into consideration in real time, thereby enhancing personalization for individual users and improving the user experience.

[0596] "User terminal" refers to an electronic device that allows a user to input, view, and operate information, including a smartphone or smart glasses.

[0597] "Purchase information" refers to detailed information about the products purchased by the user, including the purchase amount, the purchased products, the purchase date and time, and the purchase location information.

[0598] "Viewing information" refers to behavioral data of a user when viewing content, and includes the type of content viewed, viewing time, viewing frequency, and the like.

[0599] A "server" is a computing device that receives, stores, and processes data sent by users.

[0600] "Generative AI" is an artificial intelligence system that uses data stored on a server to predict specific outputs, such as optimal rewards or coupons.

[0601] "Emotional data" is data that indicates the emotional state of a user, and is collected using sensors such as a camera and a microphone.

[0602] An "emotion engine" is a system that analyzes collected emotional data and identifies the user's emotional state.

[0603] "Benefits and coupons" are incentives such as discounts and services provided to users, and are applied based on the user's purchasing or viewing behavior.

[0604] An "account" is a user identification database used to manage a user's personal information, behavioral history, benefits, coupons, etc.

[0605] "Notification" is a communication method for conveying information from the system to the user, and is done via a smartphone, smart glasses, etc.

[0606] This invention is a system that collects user viewing and purchasing information from a content distribution service used by the user, and provides optimal benefits and coupons based on this information. This system is implemented by combining a user terminal, a server, a generation AI, and an emotion engine.

[0607] First, the user terminal is an electronic device used by the user, such as a smartphone or smart glasses. When the user watches video or music content, viewing information (type of content watched, viewing time, viewing frequency, etc.) and purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) are collected. In addition, emotion data is collected using the user's emotion recognition function. This information is sent to the server.

[0608] The server receives and stores viewing information, purchase information, and emotional data sent from the user's device. This information is stored in a database on the server. Based on this stored data, the server uses generative AI to predict optimal rewards and coupons. Rewards and coupons are predicted through an integrated analysis of the user's purchase history, preference data, and emotional data.

[0609] Generative AI models such as OpenAI's GPT series are used. This generative AI analyzes viewing information, purchase information, and emotional data to predict the best rewards and coupons for users in real time. For example, incentives are provided based on the user's emotions at the time, such as a "reward for the next viewing being free" when the user is watching an enjoyable video, or a "reward for viewing points" when the user is sad.

[0610] The emotion engine analyzes emotion data acquired from the user's device and identifies the user's emotional state. For example, emotion recognition software such as the Affectiva SDK is used. The emotion data analyzed by the emotion engine is provided to the generative AI.

[0611] When a reward or coupon is predicted, the server applies it to the user's account and sends a notification to the user's device. The user's device then notifies the user of the details of the reward or coupon that has been applied, allowing the user to easily confirm that the reward or coupon has been applied.

[0612] Specific examples

[0613] Suppose a user is using a social networking app on their smartphone. While the user is watching a funny comedy show, the smartphone's camera and microphone detect the emotion "happy / laughing." This information is sent to a server, where a generative AI predicts rewards based on the following prompt:

[0614] Viewing data: {Video title: 'Funny comedy show', Viewing time: '30 minutes'}

[0615] Emotion data: {Happiness: 80%, Laughter: 70%}

[0616] User history: {Past month viewing time: '30 hours', Viewing history: 'Comedy, Action'}

[0617] Please suggest the best offer.

[0618] Based on this prompt, the AI ​​generator predicts the "next viewing free offer" and applies it to the user's account. This offer is then notified to the user's device, allowing them to use it the next time they watch.

[0619] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0620] Step 1:

[0621] The user device begins collecting the user's viewing information and emotional data. Specifically, it uses the camera and microphone installed in the smartphone or smart glasses to collect the user's viewing behavior (how much of what content was viewed) and emotional state (happiness, sadness, excitement, etc.) in real time. This data is then temporarily stored.

[0622] Input: Viewing information (type of content, viewing time, viewing frequency), emotional data (video and audio data from camera and microphone)

[0623] Output: Collected viewing information and emotional data

[0624] Step 2:

[0625] The user device transmits the collected viewing information and emotion data to the server, which receives this data and stores it in a database.

[0626] Input: Collected viewing information and emotional data

[0627] Output: Data saved on the server

[0628] Step 3:

[0629] The server inputs prompts into the generative AI model based on the stored viewing information and emotional data to predict optimal rewards and coupons. The generative AI analyzes viewing information, emotional data, and the user's past viewing history and preference data to generate rewards and coupons.

[0630] Input: Viewing information, emotional data, user's past viewing history and preference data

[0631] Output: Generated offers and coupons

[0632] Step 4:

[0633] The server receives the results from the generative AI and applies the best offers and coupons to the user's account, which are then stored in the user's account database.

[0634] Input: Rewards and coupons from generated AI

[0635] Output: Rewards and coupons applied to user account

[0636] Step 5:

[0637] The server notifies the user device of the applied benefits and coupons. The user device receives this notification and displays the details of the benefits and coupons to the user. The user can check the notification and use these benefits and coupons the next time they watch or purchase.

[0638] Input: Application result to user account

[0639] Output: Notification of special offers and coupons to user devices

[0640] This will enable a system that delivers highly personalized offers and coupons in real time based on the user's emotional state and viewing information.

[0641] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0642] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0643] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0644] [Third embodiment]

[0645] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0646] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0647] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0648] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0649] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0650] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0651] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0652] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0653] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0654] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0655] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0656] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0657] The present invention relates to a system that allows users to make purchases using optimal benefits and coupons without hassle. This system is implemented using a user terminal, a server, and a generating AI.

[0658] 1. User Device

[0659] The user device is responsible for collecting purchase information and sending it to the server. When a user purchases a product, the user device acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) and then sends the acquired purchase information to the server.

[0660] 2. Server

[0661] The server receives and stores purchase information sent from the user's device. Based on the stored purchase information, the server uses generative AI to predict the most suitable rewards and coupons. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0662] 3. Generation AI

[0663] The generation AI analyzes the purchase information provided by the server, as well as the user's purchase history and preference data. This allows the AI ​​to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. By combining the purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns it to the server.

[0664] Specific examples

[0665] For example, consider the case where User A makes a purchase of 5,000 yen at a specific shop. User A's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. The server receives this information and stores the purchase information in a database.

[0666] Next, the server passes the saved purchase information to the generation AI and asks it to predict the optimal rewards and coupons. The generation AI analyzes User A's past purchase history and preference data and predicts that a specific coupon would be optimal. For example, a "10% off coupon" or a "1,000 yen off coupon" might be considered.

[0667] The server applies the rewards and coupons to User A's account based on the predictions returned by the generation AI. The server then notifies User A of the application results on his / her device. User A can confirm that the coupon has been applied and use it the next time he / she makes a purchase.

[0668] In this way, the present invention provides a system that allows users to enjoy making purchases under optimal conditions at all times without any hassle.

[0669] The processing flow will be explained below.

[0670] Step 1:

[0671] The user completes the purchase. The user's device collects purchase information (purchase amount, purchased product, purchase date and time, and purchase location information).

[0672] Step 2:

[0673] The user terminal transmits the collected purchase information to the server.

[0674] Step 3:

[0675] The server receives the purchase information sent from the user terminal.

[0676] Step 4:

[0677] The server stores the received purchase information in a database.

[0678] Step 5:

[0679] Based on the stored purchase information, the server asks the generation AI to predict the best perks and coupons.

[0680] Step 6:

[0681] The generation AI analyzes the purchase information provided by the server as well as the user's past purchase history and preference data.

[0682] Step 7:

[0683] Based on the analysis results, the generative AI predicts the best offers and coupons for users.

[0684] Step 8:

[0685] The generation AI returns the prediction results to the server.

[0686] Step 9:

[0687] The server applies rewards and coupons to the user's account based on the predictions returned by the generation AI.

[0688] Step 10:

[0689] The server notifies the user's device of the benefits and coupon application results.

[0690] Step 11:

[0691] The user terminal receives a notification from the server and notifies the user that the coupon has been applied.

[0692] Step 12:

[0693] The user redeems the applied coupon on their next purchase.

[0694] Example 1

[0695] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0696] Today's consumers want to efficiently use a wide variety of offers and coupons when purchasing a variety of products and services, but this often requires a great deal of effort. Furthermore, providing optimal offers and coupons in real time based on purchase history and preferences requires advanced analysis, which is difficult to achieve with existing systems. As a result, users may miss out on the best offers, resulting in a poor consumer experience.

[0697] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0698] In this invention, the server includes means for collecting user purchase information from the user terminal, means for transmitting the collected purchase information to the server, means for the server to receive and store the user purchase information, means for predicting optimal benefits and coupons based on the purchase information using a generation AI, means for applying the predicted benefits and coupons to the user's account, means for notifying the user terminal of the coupon application results, means for the generation AI to analyze purchase history and preference data in real time, and means for transmitting the purchase information using a secure communication protocol. This makes it possible to automate the optimal application of benefits and coupons and improve the user's consumption experience.

[0699] A "user terminal" is a device used by consumers to collect purchase information and send it to a server, and includes smartphones, tablets, etc.

[0700] "Server" means a computer system that stores and analyzes purchase information received from a user's device and manages the application of special offers and coupons.

[0701] "Purchase information" refers to detailed information when a user purchases a product or service, including the purchase amount, purchased product, purchase date and time, and purchase location information.

[0702] "Generative AI" is an artificial intelligence system that predicts optimal benefits and coupons based on a user's purchase information, purchase history, and preference data.

[0703] "Benefits and coupons" refers to discounts and services provided to users in relation to their purchases, including coupons that provide a partial discount on the purchase price and points.

[0704] "Purchase history" is cumulative data about purchases made by a user in the past.

[0705] "Preference data" is information about a user's preferences and habits, including data on the products and services the user prefers to purchase and the stores they frequently visit.

[0706] A "secure communication protocol" is a set of communication rules for encrypting and sending digital information, maintaining the confidentiality and integrity of the data, such as HTTPS.

[0707] "Real-time" refers to processing or responding to an event within the same time it occurs, meaning that it is applied immediately without delay.

[0708] The present invention relates to a system that allows users to make purchases using optimal benefits and coupons without hassle. The system is implemented using a user terminal, a server, and a generating AI.

[0709] User device details:

[0710] The user device is responsible for collecting purchase information and sending it to the server. Specifically, when a user purchases a product, the user device obtains the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.). The hardware used is typically a smartphone or tablet. The user device then sends the collected information to the server via the internet. To ensure security, it is recommended to use a secure communication protocol such as HTTPS.

[0711] Server details:

[0712] The server receives purchase information sent from the user's device and stores it in a database. Examples of database systems used include MySQL and PostgreSQL. Based on the stored purchase information, the server uses generative AI to predict the most suitable rewards and coupons. Software libraries such as Python and TensorFlow can be used for the generative AI. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0713] More about generative AI:

[0714] The generation AI analyzes the purchase information provided by the server and the user's purchase history and preference data to predict the best offers and coupons for each user. This analysis utilizes machine learning libraries such as Python and TensorFlow. By combining purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns the selection results to the server.

[0715] Examples:

[0716] For example, consider the case where User A makes a purchase of 5,000 yen at a specific shop. User A's device (e.g., a smartphone) collects information about the purchase amount, purchased items, purchase date and time, and purchase location, and sends this information to a server via the Internet. The server receives this information and stores it in a database (e.g., MySQL).

[0717] Next, the server passes the saved purchase information to the generation AI and asks it to predict the most suitable perks and coupons. The generation AI analyzes User A's past purchase history and preference data, and predicts that, for example, a "10% off coupon" or a "1,000 yen off coupon" would be optimal. The server applies this to User A's account and notifies User A's device of the application result. User A can check the notification and use the perks and coupons the next time they make a purchase.

[0718] Example prompt sentence:

[0719] "Please create a prompt for the generative AI to predict the best coupon based on the product and purchase amount purchased by the user."

[0720] "Please provide prompts for the generative AI to suggest the best offers based on your past purchase history and current purchase information."

[0721] In this way, the present invention realizes a system that automatically provides convenient and effective benefits and coupons to users, improving their consumption experience.

[0722] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0723] Step 1: User purchases a product

[0724] A user purchases a product in a store or online shop, selects the product, and completes the purchase process.

[0725] Input: Select product and complete purchase

[0726] Output: Purchase information (purchase amount, purchased item, purchase date and time, purchase location, etc.)

[0727] Step 2: The user device collects purchase information

[0728] After a purchase, the user's device collects purchase information, such as the purchase amount, purchased items, purchase date and time, and purchase location.

[0729] Input: Purchase information (purchase amount, purchased item, purchase date and time, purchase location, etc.)

[0730] Output: Collected purchase information

[0731] Step 3: The user device sends the purchase information to the server

[0732] The user device sends the collected purchase information to a server via the Internet, using a secure communication protocol such as HTTPS to ensure security.

[0733] Input: Collected purchase information

[0734] Output: Purchase information sent to the server

[0735] Step 4: The server receives the purchase information and stores it in a database

[0736] The server receives the purchase information sent from the user's device and stores it in a database, typically using a database system such as MySQL or PostgreSQL.

[0737] Input: Purchase information sent to the server

[0738] Output: Purchase information stored in a database

[0739] Step 5: The server passes the purchase information to the generation AI to predict the best offers and coupons.

[0740] The server passes the purchase information stored in the database to the generation AI, which predicts optimal rewards and coupons based on the purchase information, past purchase history, and preference data.

[0741] Input: Purchase information stored in a database, past purchase history, and preference data

[0742] Output: Predicted best offers and coupons

[0743] Step 6: The generative AI returns the prediction results to the server

[0744] The generation AI returns the results of the data analysis to the server. Specifically, it predicts things like a "10% off coupon" or a "1,000 yen off coupon."

[0745] Input: Best offers and coupon predictions

[0746] Output: Prediction results returned to the server

[0747] Step 7: The server applies rewards or coupons to the user's account based on the predictions.

[0748] The server automatically applies rewards and coupons to the user's account based on the predictions received from the generation AI and updates the database.

[0749] Input: Prediction result returned to the server

[0750] Output: User account with rewards and coupons applied

[0751] Step 8: The server notifies the user device of the benefits and coupon application results.

[0752] The server notifies the user device that the reward or coupon has been applied to the user's account, via push notification, email, or other means.

[0753] Input: User account to which the reward or coupon was applied

[0754] Output: Application results notified to the user's terminal

[0755] Step 9: User checks and redeems offers and coupons

[0756] Users can check the notification sent to their device and use the rewards and coupons on their next purchase. Specifically, they can check the reward details on the smartphone app and present it to receive the discount.

[0757] Input: Application result notified to the user terminal

[0758] Output: Next purchase experience using rewards and coupons

[0759] (Application example 1)

[0760] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0761] Conventional coupon and reward systems make it difficult for users to receive the most appropriate rewards in real time without any effort. This can lead to users overlooking coupons and rewards or having to spend a lot of time and effort to find the best rewards. This problem is particularly pronounced when purchasing in a physical store. If users could receive the most appropriate rewards and coupons in real time while shopping in a physical store, it would improve the user's purchasing experience and increase sales for the store.

[0762] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0763] In this invention, the server includes means for collecting user purchase information from the user terminal, means for transmitting the collected purchase information to the server, means for the server to receive and store the user purchase information, means for predicting optimal benefits and coupons based on the purchase information using a generation AI, means for applying the predicted benefits and coupons to the user's account, means for notifying the user terminal of the coupon application result, and means including a smart device for providing optimal coupons and benefits in real time when the user makes a purchase at a physical store. This allows the user to receive optimal benefits and coupons in real time while shopping at a physical store without any hassle.

[0764] "User terminal" refers to a portable electronic device used by a user, such as a smartphone, smart glasses, or head-mounted display.

[0765] The "means of collection" refers to a function that acquires purchase information (purchase amount, purchased item, purchase date and time, purchase location information, etc.) when a user purchases a product and transmits the data to a server.

[0766] A "server" is a computer system that receives and stores purchase information sent from a user terminal.

[0767] "Generative AI" is artificial intelligence that analyzes users' purchase information, purchase history, and preference data.

[0768] "Means for predicting benefits and coupons" refers to a function that uses generative AI to select the most suitable benefits and coupons for users.

[0769] "Means of applying to a user's account" refers to the function of registering the benefits and coupons predicted by the generation AI to a user's account.

[0770] "Means of notification" refers to a function that notifies the user device that a benefit or coupon has been applied.

[0771] "Real-time delivery" refers to the ability to instantly provide rewards and coupons to users when they make a purchase in a physical store.

[0772] "Smart devices" refer to electronic devices that can be worn or carried by users, and primarily include smartphones, smart glasses, head-mounted displays, etc.

[0773] The present invention is a system that allows users to easily receive optimal rewards and coupons in real time while shopping in a physical store. This system is implemented using a user terminal, a server, and a generation AI.

[0774] 1. User Device

[0775] The user device is a portable electronic device such as a smartphone or smart glasses. When a user purchases an item in a store, the device acquires purchase information (purchase amount, purchased item, purchase date and time, purchase location information) and sends it to the server. This process is performed in real time, allowing the user to receive optimal benefits and coupons while in the store.

[0776] 2. Server

[0777] The server receives purchase information sent from the user's device and stores it in a database. It also provides the purchase information to the generation AI and requests it to predict the best rewards and coupons. The server may use database software such as SQLite or MySQL. Once the best rewards and coupons are predicted, they are automatically applied to the user's account.

[0778] 3. Generation AI

[0779] The Generative AI analyzes the purchase information provided by the server, as well as the user's past purchase history and preference data. This allows it to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. The Generative AI uses advanced artificial intelligence technologies such as the OpenAI API.

[0780] Specific examples

[0781] For example, if a user makes a purchase of 5,000 yen at a specific physical store, the user's device collects this purchase information. The purchase amount, purchased items, purchase date and time, and purchase location information are acquired and sent to the server. The server receives this information and stores it in a database. The server then passes this stored purchase information to the generation AI and requests it to predict the most appropriate perks and coupons. The generation AI analyzes past purchase history and preference data and predicts that, for example, a "10% off coupon" or a "1,000 yen off coupon" would be optimal. The server applies the perks and coupons to the user's account based on the prediction results from the generation AI and notifies the user's device of the application results.

[0782] Example prompts for generative AI models

[0783] User ID: user123

[0784] Purchase information: Purchase amount 5,000 yen, product ["Product A", "Product B"], date and time 2023-10-01 14:00, store "Store X"

[0785] Past purchase history: [Past purchase data]

[0786] Predict the best coupons.”

[0787] This allows users to receive the most suitable offers and coupons in real time while shopping in a physical store without any hassle, improving the user's purchasing experience and also bringing the benefit of increased sales to the store.

[0788] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0789] Step 1:

[0790] A user purchases a product at a physical store. The user's device (smartphone or smart glasses) collects purchase information in real time. Specifically, it acquires the purchase amount, purchased product, purchase date and time, and purchase location information. The input is the user's detailed purchase information, which the device collects as data.

[0791] Step 2:

[0792] The user terminal sends the collected purchase information to the server. The input is the purchase information collected in step 1, which the terminal prepares as transmission data and sends to the server over the network. The output is the purchase information sent to the server.

[0793] Step 3:

[0794] The server receives the purchase information sent from the user terminal and stores it in a database. The input is the purchase information sent from the user terminal, which the server takes as received data, converts into an appropriate format, and stores in the database. The output is the purchase information stored in the database.

[0795] Step 4:

[0796] The server sends the stored purchase information to the generation AI and requests it to predict the optimal perks and coupons. The input is the purchase information stored in the database, which the server formats for the generation AI and sends. The generation AI performs data analysis based on the input data and predicts the optimal perks and coupons. The output is the predicted perks and coupons.

[0797] Step 5:

[0798] The server applies rewards and coupons to the user's account based on the predictions returned by the generation AI. The input is the predictions returned by the generation AI, which the server processes as data to apply to the user's account. The output is the rewards and coupons applied to the user's account.

[0799] Step 6:

[0800] The server notifies the user terminal of the results of the coupon or reward applied to the user account. The input is the reward or coupon information applied to the user account, which the server prepares as a notification message and sends to the user terminal. The output is the user terminal that received the notification.

[0801] Step 7:

[0802] The user device receives notifications from the server and displays the results of coupon and reward application to the user. The input is the notification message sent from the server, which the device renders to visually present to the user. The output is the notification displayed to the user.

[0803] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0804] The present invention combines a system that allows users to make purchases using optimal benefits and coupons without hassle with an emotion engine that recognizes user emotions. This system is implemented using a user terminal, a server, a generation AI, and an emotion engine.

[0805] 1. User Device

[0806] The user device is responsible for collecting purchase information and sending it to the server. When a user purchases a product, the user device acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.). The acquired purchase information is then sent to the server. The user device also has an interface for recognizing the user's emotions and sends emotional data to the emotion engine.

[0807] 2. Server

[0808] The server receives and stores purchase information and emotion data sent from the user's device. Based on the stored data, the server uses generative AI to predict optimal rewards and coupons. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0809] 3. Generation AI

[0810] The generation AI analyzes the purchase information provided by the server and the user's purchase history and preference data. It also takes into account the emotional data provided by the emotion engine to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. By combining this with the purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns it to the server.

[0811] 4. Emotion Engine

[0812] The emotion engine is a system for recognizing user emotions and analyzes emotion data collected from the user's device. The results of the emotion engine's analysis are provided to the generative AI and used to predict rewards and coupons.

[0813] Specific examples

[0814] For example, consider the case where User B makes a purchase of 5,000 yen at a specific shop. User B's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. Emotional data, such as whether the user was happy with the purchase, is also collected at the same time.

[0815] The server receives this information and emotional data and stores the purchase information in a database. The server then passes the stored data to the generation AI, asking it to predict the optimal rewards and coupons. The generation AI analyzes User B's past purchase history, preference data, and emotional data, and predicts that a specific coupon will be optimal. For example, if the user is happy, it might consider an "additional 10% off coupon," or if the user is sad, it might consider a "1,000 yen off coupon."

[0816] The server applies the rewards and coupons to User B's account based on the predictions returned by the generation AI. The server then notifies User B of the application results. User B can confirm that the coupon has been applied and use it the next time they make a purchase.

[0817] In this way, the present invention provides a system that allows users to enjoy shopping without hassle by using optimal benefits and coupons according to their emotional state.

[0818] The processing flow will be explained below.

[0819] Step 1:

[0820] The user completes the purchase. The user's device collects purchase information (purchase amount, purchased product, purchase date and time, and purchase location information).

[0821] Step 2:

[0822] The user's device collects purchase information along with emotional data from the user, which is acquired through an emotion engine based on the user's facial expressions, tone of voice, etc.

[0823] Step 3:

[0824] The user's device sends the collected purchase information and emotion data to the server.

[0825] Step 4:

[0826] The server receives the purchase information and emotion data sent from the user terminal.

[0827] Step 5:

[0828] The server stores the received purchase information and emotion data in a database.

[0829] Step 6:

[0830] The server provides the stored purchase information and emotional data to the generation AI and asks it to predict the best offers and coupons.

[0831] Step 7:

[0832] The generative AI analyzes purchase information, emotional data, and past purchase history and preference data provided by the server.

[0833] Step 8:

[0834] Based on the analysis results, the generative AI predicts the most appropriate rewards and coupons, taking into account the user's current emotional state. For example, if the user is happy, it will select an "additional 10% off coupon," and if they are sad, it will select a "1,000 yen off coupon."

[0835] Step 9:

[0836] The generation AI returns the prediction results to the server.

[0837] Step 10:

[0838] The server applies rewards and coupons to the user's account based on the predictions returned by the generative AI.

[0839] Step 11:

[0840] The server notifies the user terminal of the results of applying the benefits and coupons.

[0841] Step 12:

[0842] The user terminal receives a notification from the server and notifies the user that the coupon has been applied.

[0843] Step 13:

[0844] The user redeems the applied coupon on their next purchase.

[0845] Example 2

[0846] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0847] Conventional coupon and reward systems only consider users' purchase information, and are therefore unable to provide optimal rewards and coupons that reflect the user's emotional state. This can result in a lack of improvement in the user's purchasing experience and a decrease in satisfaction with the system.

[0848] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0849] In this invention, the server includes means for collecting user purchase information and emotional data from the user terminal, means for receiving and storing the purchase information and emotional data, means for predicting optimal benefits and coupons based on the purchase information and emotional data using a generation AI, means for applying the predicted benefits and coupons to the user's account, and means for notifying the user terminal of the coupon application results, thereby making it possible to provide optimal benefits and coupons that take into account the user's emotional state.

[0850] A "user terminal" is a device used by a user when purchasing a product, and is a device that collects purchase information and emotional data and transmits them to a server.

[0851] "Purchase information" refers to information relating to a transaction that occurs when a user purchases a product, and specifically includes data such as the purchase amount, the purchased product, the purchase date and time, and the purchase location.

[0852] "Emotion data" is data that indicates the user's emotional state, and includes information obtained from the user's facial expressions and voice.

[0853] The "server" is a central processing unit that receives and stores data sent from user devices, and then works with the generation AI to predict optimal benefits and coupons and provide them to users.

[0854] "Generative AI" is an artificial intelligence model that analyzes data stored on a server and predicts optimal rewards and coupons based on users' purchase information and emotional data.

[0855] "Special Offers and Coupons" refers to discounts and other preferential treatments that are applied when a user purchases a product, and are incentives to improve the user's purchasing experience.

[0856] The "means for applying predicted rewards and coupons to a user's account" refers to a system that processes the rewards and coupons predicted by the generating AI to associate them with a user's unique digital account.

[0857] "Means for notifying the user terminal of the results of applying a coupon" refers to a communication method for informing the user terminal that a benefit or coupon has been applied to the user's account, and is a system that has a feedback function to the user.

[0858] This invention is a system that helps users make optimal choices when using special offers and coupons without hassle. It is particularly characterized by combining a user's purchase information with emotional data to provide individually optimized special offers and coupons. This system is realized by combining a user terminal, a server, a generation AI, and an emotion engine.

[0859] 1. User Device

[0860] The user terminal collects information when the user makes a purchase and sends it to the server. It also has an interface for recognizing the user's emotions and sends the emotional data to the emotion engine. This terminal is usually implemented as a device such as a smartphone, tablet, or POS system.

[0861] When a user purchases a product, the user's device acquires purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) and sends this information to the server. The device also collects the user's facial expressions and voice, and sends this as emotion data to the emotion engine. For example, if a user purchases 5,000 yen worth of groceries at a supermarket and is smiling, the emotion data of "joy" is sent along with the purchase amount, purchased product, and location information.

[0862] 2. Server

[0863] The server receives the purchase information and emotion data and stores them in a database. Based on this data, the server generates a prompt message that asks the AI ​​to predict rewards and coupons. For example, a prompt message might be created such as, "User B made a purchase worth 5,000 yen and expressed joy. Please predict the best coupon based on this information."

[0864] The server applies the prediction results (rewards and coupons) returned by the generation AI to the user's account and notifies the user of the results, allowing the user to know which coupons are available for their next purchase.

[0865] 3. Generation AI

[0866] The generation AI analyzes purchase information and sentiment data provided by the server to predict the best rewards and coupons for each user. This analysis also takes into account the user's past purchase history and preference data. The generation AI makes predictions in real time and selects the most effective coupons under specific conditions.

[0867] For example, if the user has a history of making purchases from the same store in the past and expressed the emotion of "joy" at that time, the AI ​​will predict that an "additional 10% off coupon" would be optimal. This prediction result is returned to the server, where it is actually applied.

[0868] 4. Emotion Engine

[0869] The emotion engine is software that recognizes user emotions and analyzes emotional data collected from the user's device. The analysis results are provided to the generation AI, which then reflects the emotional data in predicting rewards and coupons, enabling the provision of coupons that are more optimal for the user.

[0870] The emotion engine analyzes the user's emotional state, such as "joy," "sadness," or "anger," and the generative AI then selects the most appropriate rewards and coupons.

[0871] Specific examples

[0872] When User B makes a purchase of 5,000 yen at a specific shop, User B's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. User emotional data is also collected at the same time.

[0873] The server receives this information and emotion data and stores the purchase information in a database. The server then asks the generation AI to predict rewards and coupons based on the generated prompt. The generation AI analyzes User B's past purchase history, preference data, and emotion data to predict which specific coupon is most appropriate. For example, if the user is happy, an "additional 10% off coupon" is considered, and if the user is sad, a "1,000 yen off coupon" is considered.

[0874] The server applies the rewards and coupons to User B's account based on the predictions returned by the generation AI. The server then notifies User B of the application results on his / her device. User B will receive a notification that the coupon has been applied, and can use it the next time he / she makes a purchase.

[0875] The above is an embodiment of the present invention. This system allows users to enjoy shopping without hassle by using the best offers and coupons according to their emotional state.

[0876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0877] Step 1:

[0878] The user's device collects purchase information.

[0879] When a user purchases a product, the user's device automatically acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location, etc.). Specifically, this data is collected from POS systems and online purchase screens. The input is the user's purchase action, and the output is the collected purchase information.

[0880] Step 2:

[0881] The user's device collects emotional data.

[0882] The user device uses a built-in camera and microphone to record the user's facial expressions and voice at the time of purchase and analyzes them as emotional data. The input is the user's facial expressions and voice, and the output is the analyzed emotional data.

[0883] Step 3:

[0884] The user terminal transmits the purchase information and emotion data to the server.

[0885] The collected purchase information and emotion data are sent in encrypted form from the user's device to the server. The input is the purchase information and emotion data, and the output is a transmission completion notification to the server.

[0886] Step 4:

[0887] A server stores purchase information and emotion data.

[0888] The server stores the received purchase information and sentiment data in a database, which is important for the generative AI to analyze the data later. The input is the transmitted data, and the output is the stored data.

[0889] Step 5:

[0890] The server generates a prompt message that asks the AI ​​to predict rewards and coupons.

[0891] The server generates a prompt based on the stored data and sends it to the generation AI. For example, the prompt might be something like, "User B made a purchase worth 5,000 yen and expressed happiness. Please predict the best coupon based on this information." The input is the stored data, and the output is the generated prompt.

[0892] Step 6:

[0893] Generative AI analyzes purchase information and sentiment data to predict the best offers and coupons.

[0894] The generative AI reads data from the prompt and predicts rewards and coupons based on the user's past history and preferences. The input is the prompt and related data, and the output is the predicted rewards and coupons.

[0895] Step 7:

[0896] The server applies the generated offers and coupons to the user's account.

[0897] The server applies the coupon to the user's account based on the coupon information returned by the generation AI. The input is the predicted coupon information, and the output is a notification that the coupon has been applied to the user's account.

[0898] Step 8:

[0899] The server notifies the user terminal of the coupon application result.

[0900] The server sends the coupon application result in the form of a notification to the user terminal to inform the user. The input is the coupon application completion notification, and the output is the notification to the user terminal.

[0901] This is the specific processing flow of the program for this system. Through this step, users can receive the best rewards and coupons according to their emotional state.

[0902] (Application example 2)

[0903] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0904] Modern content distribution services face challenges in allowing users to easily receive personalized rewards and coupons. Furthermore, traditional reward and coupon offerings fail to consider the user's emotional state, making it difficult to maximize user satisfaction. Furthermore, there is a demand for more accurate personalization through the integrated use of both viewing data and purchase data. The present invention aims to provide a system that solves these challenges and improves the user experience.

[0905] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user purchase information and viewing information from the user terminal, means for transmitting the collected purchase information and viewing information to the server, means for the server to receive and store the user purchase information and viewing information, means for predicting optimal benefits and coupons based on the purchase information and viewing information using a generation AI, means for collecting and analyzing user emotion data and applying benefits and coupons based on the emotions, means for applying the predicted benefits and coupons to the user's account, and means for notifying the user terminal of the coupon application results. This makes it possible to provide optimal benefits and coupons that take the user's emotional state into consideration in real time, thereby enhancing personalization for individual users and improving the user experience.

[0906] "User terminal" refers to an electronic device that allows a user to input, view, and operate information, including a smartphone or smart glasses.

[0907] "Purchase information" refers to detailed information about the products purchased by the user, including the purchase amount, the purchased products, the purchase date and time, and the purchase location information.

[0908] "Viewing information" refers to behavioral data of a user when viewing content, and includes the type of content viewed, viewing time, viewing frequency, and the like.

[0909] A "server" is a computing device that receives, stores, and processes data sent by users.

[0910] "Generative AI" is an artificial intelligence system that uses data stored on a server to predict specific outputs, such as optimal rewards or coupons.

[0911] "Emotional data" is data that indicates the emotional state of a user, and is collected using sensors such as a camera and a microphone.

[0912] An "emotion engine" is a system that analyzes collected emotional data and identifies the user's emotional state.

[0913] "Benefits and coupons" are incentives such as discounts and services provided to users, and are applied based on the user's purchasing or viewing behavior.

[0914] An "account" is a user identification database used to manage a user's personal information, behavioral history, benefits, coupons, etc.

[0915] "Notification" is a communication method for conveying information from the system to the user, and is done via a smartphone, smart glasses, etc.

[0916] This invention is a system that collects user viewing and purchasing information from a content distribution service used by the user, and provides optimal benefits and coupons based on this information. This system is implemented by combining a user terminal, a server, a generation AI, and an emotion engine.

[0917] First, the user terminal is an electronic device used by the user, such as a smartphone or smart glasses. When the user watches video or music content, viewing information (type of content watched, viewing time, viewing frequency, etc.) and purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) are collected. In addition, emotion data is collected using the user's emotion recognition function. This information is sent to the server.

[0918] The server receives and stores viewing information, purchase information, and emotional data sent from the user's device. This information is stored in a database on the server. Based on this stored data, the server uses generative AI to predict optimal rewards and coupons. Rewards and coupons are predicted through an integrated analysis of the user's purchase history, preference data, and emotional data.

[0919] Generative AI models such as OpenAI's GPT series are used. This generative AI analyzes viewing information, purchase information, and emotional data to predict the best rewards and coupons for users in real time. For example, incentives are provided based on the user's emotions at the time, such as a "reward for the next viewing being free" when the user is watching an enjoyable video, or a "reward for viewing points" when the user is sad.

[0920] The emotion engine analyzes emotion data acquired from the user's device and identifies the user's emotional state. For example, emotion recognition software such as the Affectiva SDK is used. The emotion data analyzed by the emotion engine is provided to the generative AI.

[0921] When a reward or coupon is predicted, the server applies it to the user's account and sends a notification to the user's device. The user's device then notifies the user of the details of the reward or coupon that has been applied, allowing the user to easily confirm that the reward or coupon has been applied.

[0922] Specific examples

[0923] Suppose a user is using a social networking app on their smartphone. While the user is watching a funny comedy show, the smartphone's camera and microphone detect the emotion "happy / laughing." This information is sent to a server, where a generative AI predicts rewards based on the following prompt:

[0924] Viewing data: {Video title: 'Funny comedy show', Viewing time: '30 minutes'}

[0925] Emotion data: {Happiness: 80%, Laughter: 70%}

[0926] User history: {Past month viewing time: '30 hours', Viewing history: 'Comedy, Action'}

[0927] Please suggest the best offer.

[0928] Based on this prompt, the AI ​​generator predicts the "next viewing free offer" and applies it to the user's account. This offer is then notified to the user's device, allowing them to use it the next time they watch.

[0929] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0930] Step 1:

[0931] The user device begins collecting the user's viewing information and emotional data. Specifically, it uses the camera and microphone installed in the smartphone or smart glasses to collect the user's viewing behavior (how much of what content was viewed) and emotional state (happiness, sadness, excitement, etc.) in real time. This data is then temporarily stored.

[0932] Input: Viewing information (type of content, viewing time, viewing frequency), emotional data (video and audio data from camera and microphone)

[0933] Output: Collected viewing information and emotional data

[0934] Step 2:

[0935] The user device transmits the collected viewing information and emotion data to the server, which receives this data and stores it in a database.

[0936] Input: Collected viewing information and emotional data

[0937] Output: Data saved on the server

[0938] Step 3:

[0939] The server inputs prompts into the generative AI model based on the stored viewing information and emotional data to predict optimal rewards and coupons. The generative AI analyzes viewing information, emotional data, and the user's past viewing history and preference data to generate rewards and coupons.

[0940] Input: Viewing information, emotional data, user's past viewing history and preference data

[0941] Output: Generated offers and coupons

[0942] Step 4:

[0943] The server receives the results from the generative AI and applies the best offers and coupons to the user's account, which are then stored in the user's account database.

[0944] Input: Rewards and coupons from generated AI

[0945] Output: Rewards and coupons applied to user account

[0946] Step 5:

[0947] The server notifies the user device of the applied benefits and coupons. The user device receives this notification and displays the details of the benefits and coupons to the user. The user can check the notification and use these benefits and coupons the next time they watch or purchase.

[0948] Input: Application result to user account

[0949] Output: Notification of special offers and coupons to user devices

[0950] This will enable a system that delivers highly personalized offers and coupons in real time based on the user's emotional state and viewing information.

[0951] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0952] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0953] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0954] [Fourth embodiment]

[0955] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0956] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0957] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0958] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0959] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0960] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0961] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0962] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0963] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0964] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0965] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0966] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0967] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0968] The present invention relates to a system that allows users to make purchases using optimal benefits and coupons without hassle. This system is implemented using a user terminal, a server, and a generating AI.

[0969] 1. User Device

[0970] The user device is responsible for collecting purchase information and sending it to the server. When a user purchases a product, the user device acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) and then sends the acquired purchase information to the server.

[0971] 2. Server

[0972] The server receives and stores purchase information sent from the user's device. Based on the stored purchase information, the server uses generative AI to predict the most suitable rewards and coupons. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[0973] 3. Generation AI

[0974] The generation AI analyzes the purchase information provided by the server, as well as the user's purchase history and preference data. This allows the AI ​​to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. By combining the purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns it to the server.

[0975] Specific examples

[0976] For example, consider the case where User A makes a purchase of 5,000 yen at a specific shop. User A's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. The server receives this information and stores the purchase information in a database.

[0977] Next, the server passes the saved purchase information to the generation AI and asks it to predict the optimal rewards and coupons. The generation AI analyzes User A's past purchase history and preference data and predicts that a specific coupon would be optimal. For example, a "10% off coupon" or a "1,000 yen off coupon" might be considered.

[0978] The server applies the rewards and coupons to User A's account based on the predictions returned by the generation AI. The server then notifies User A of the application results on his / her device. User A can confirm that the coupon has been applied and use it the next time he / she makes a purchase.

[0979] In this way, the present invention provides a system that allows users to enjoy making purchases under optimal conditions at all times without any hassle.

[0980] The processing flow will be explained below.

[0981] Step 1:

[0982] The user completes the purchase. The user's device collects purchase information (purchase amount, purchased product, purchase date and time, and purchase location information).

[0983] Step 2:

[0984] The user terminal transmits the collected purchase information to the server.

[0985] Step 3:

[0986] The server receives the purchase information sent from the user terminal.

[0987] Step 4:

[0988] The server stores the received purchase information in a database.

[0989] Step 5:

[0990] Based on the stored purchase information, the server asks the generation AI to predict the best perks and coupons.

[0991] Step 6:

[0992] The generation AI analyzes the purchase information provided by the server as well as the user's past purchase history and preference data.

[0993] Step 7:

[0994] Based on the analysis results, the generative AI predicts the best offers and coupons for users.

[0995] Step 8:

[0996] The generation AI returns the prediction results to the server.

[0997] Step 9:

[0998] The server applies rewards and coupons to the user's account based on the predictions returned by the generation AI.

[0999] Step 10:

[1000] The server notifies the user's device of the benefits and coupon application results.

[1001] Step 11:

[1002] The user terminal receives a notification from the server and notifies the user that the coupon has been applied.

[1003] Step 12:

[1004] The user redeems the applied coupon on their next purchase.

[1005] Example 1

[1006] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1007] Today's consumers want to efficiently use a wide variety of offers and coupons when purchasing a variety of products and services, but this often requires a great deal of effort. Furthermore, providing optimal offers and coupons in real time based on purchase history and preferences requires advanced analysis, which is difficult to achieve with existing systems. As a result, users may miss out on the best offers, resulting in a poor consumer experience.

[1008] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1009] In this invention, the server includes means for collecting user purchase information from the user terminal, means for transmitting the collected purchase information to the server, means for the server to receive and store the user purchase information, means for predicting optimal benefits and coupons based on the purchase information using a generation AI, means for applying the predicted benefits and coupons to the user's account, means for notifying the user terminal of the coupon application results, means for the generation AI to analyze purchase history and preference data in real time, and means for transmitting the purchase information using a secure communication protocol. This makes it possible to automate the optimal application of benefits and coupons and improve the user's consumption experience.

[1010] A "user terminal" is a device used by consumers to collect purchase information and send it to a server, and includes smartphones, tablets, etc.

[1011] "Server" means a computer system that stores and analyzes purchase information received from a user's device and manages the application of special offers and coupons.

[1012] "Purchase information" refers to detailed information when a user purchases a product or service, including the purchase amount, purchased product, purchase date and time, and purchase location information.

[1013] "Generative AI" is an artificial intelligence system that predicts optimal benefits and coupons based on a user's purchase information, purchase history, and preference data.

[1014] "Benefits and coupons" refers to discounts and services provided to users in relation to their purchases, including coupons that provide a partial discount on the purchase price and points.

[1015] "Purchase history" is cumulative data about purchases made by a user in the past.

[1016] "Preference data" is information about a user's preferences and habits, including data on the products and services the user prefers to purchase and the stores they frequently visit.

[1017] A "secure communication protocol" is a set of communication rules for encrypting and sending digital information, maintaining the confidentiality and integrity of the data, such as HTTPS.

[1018] "Real-time" refers to processing or responding to an event within the same time it occurs, meaning that it is applied immediately without delay.

[1019] The present invention relates to a system that allows users to make purchases using optimal benefits and coupons without hassle. The system is implemented using a user terminal, a server, and a generating AI.

[1020] User device details:

[1021] The user device is responsible for collecting purchase information and sending it to the server. Specifically, when a user purchases a product, the user device obtains the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.). The hardware used is typically a smartphone or tablet. The user device then sends the collected information to the server via the internet. To ensure security, it is recommended to use a secure communication protocol such as HTTPS.

[1022] Server details:

[1023] The server receives purchase information sent from the user's device and stores it in a database. Examples of database systems used include MySQL and PostgreSQL. Based on the stored purchase information, the server uses generative AI to predict the most suitable rewards and coupons. Software libraries such as Python and TensorFlow can be used for the generative AI. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[1024] More about generative AI:

[1025] The generation AI analyzes the purchase information provided by the server and the user's purchase history and preference data to predict the best offers and coupons for each user. This analysis utilizes machine learning libraries such as Python and TensorFlow. By combining purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns the selection results to the server.

[1026] Examples:

[1027] For example, consider the case where User A makes a purchase of 5,000 yen at a specific shop. User A's device (e.g., a smartphone) collects information about the purchase amount, purchased items, purchase date and time, and purchase location, and sends this information to a server via the Internet. The server receives this information and stores it in a database (e.g., MySQL).

[1028] Next, the server passes the saved purchase information to the generation AI and asks it to predict the most suitable perks and coupons. The generation AI analyzes User A's past purchase history and preference data, and predicts that, for example, a "10% off coupon" or a "1,000 yen off coupon" would be optimal. The server applies this to User A's account and notifies User A's device of the application result. User A can check the notification and use the perks and coupons the next time they make a purchase.

[1029] Example prompt sentence:

[1030] "Please create a prompt for the generative AI to predict the best coupon based on the product and purchase amount purchased by the user."

[1031] "Please provide prompts for the generative AI to suggest the best offers based on your past purchase history and current purchase information."

[1032] In this way, the present invention realizes a system that automatically provides convenient and effective benefits and coupons to users, improving their consumption experience.

[1033] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1034] Step 1: User purchases a product

[1035] A user purchases a product in a store or online shop, selects the product, and completes the purchase process.

[1036] Input: Select product and complete purchase

[1037] Output: Purchase information (purchase amount, purchased item, purchase date and time, purchase location, etc.)

[1038] Step 2: The user device collects purchase information

[1039] After a purchase, the user's device collects purchase information, such as the purchase amount, purchased items, purchase date and time, and purchase location.

[1040] Input: Purchase information (purchase amount, purchased item, purchase date and time, purchase location, etc.)

[1041] Output: Collected purchase information

[1042] Step 3: The user device sends the purchase information to the server

[1043] The user device sends the collected purchase information to a server via the Internet, using a secure communication protocol such as HTTPS to ensure security.

[1044] Input: Collected purchase information

[1045] Output: Purchase information sent to the server

[1046] Step 4: The server receives the purchase information and stores it in a database

[1047] The server receives the purchase information sent from the user's device and stores it in a database, typically using a database system such as MySQL or PostgreSQL.

[1048] Input: Purchase information sent to the server

[1049] Output: Purchase information stored in a database

[1050] Step 5: The server passes the purchase information to the generation AI to predict the best offers and coupons.

[1051] The server passes the purchase information stored in the database to the generation AI, which predicts optimal rewards and coupons based on the purchase information, past purchase history, and preference data.

[1052] Input: Purchase information stored in a database, past purchase history, and preference data

[1053] Output: Predicted best offers and coupons

[1054] Step 6: The generative AI returns the prediction results to the server

[1055] The generation AI returns the results of the data analysis to the server. Specifically, it predicts things like a "10% off coupon" or a "1,000 yen off coupon."

[1056] Input: Best offers and coupon predictions

[1057] Output: Prediction results returned to the server

[1058] Step 7: The server applies rewards or coupons to the user's account based on the predictions.

[1059] The server automatically applies rewards and coupons to the user's account based on the predictions received from the generation AI and updates the database.

[1060] Input: Prediction result returned to the server

[1061] Output: User account with rewards and coupons applied

[1062] Step 8: The server notifies the user device of the benefits and coupon application results.

[1063] The server notifies the user device that the reward or coupon has been applied to the user's account, via push notification, email, or other means.

[1064] Input: User account to which the reward or coupon was applied

[1065] Output: Application results notified to the user's terminal

[1066] Step 9: User checks and redeems offers and coupons

[1067] Users can check the notification sent to their device and use the rewards and coupons on their next purchase. Specifically, they can check the reward details on the smartphone app and present it to receive the discount.

[1068] Input: Application result notified to the user terminal

[1069] Output: Next purchase experience using rewards and coupons

[1070] (Application example 1)

[1071] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1072] Conventional coupon and reward systems make it difficult for users to receive the most appropriate rewards in real time without any effort. This can lead to users overlooking coupons and rewards or having to spend a lot of time and effort to find the best rewards. This problem is particularly pronounced when purchasing in a physical store. If users could receive the most appropriate rewards and coupons in real time while shopping in a physical store, it would improve the user's purchasing experience and increase sales for the store.

[1073] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1074] In this invention, the server includes means for collecting user purchase information from the user terminal, means for transmitting the collected purchase information to the server, means for the server to receive and store the user purchase information, means for predicting optimal benefits and coupons based on the purchase information using a generation AI, means for applying the predicted benefits and coupons to the user's account, means for notifying the user terminal of the coupon application result, and means including a smart device for providing optimal coupons and benefits in real time when the user makes a purchase at a physical store. This allows the user to receive optimal benefits and coupons in real time while shopping at a physical store without any hassle.

[1075] "User terminal" refers to a portable electronic device used by a user, such as a smartphone, smart glasses, or head-mounted display.

[1076] The "means of collection" refers to a function that acquires purchase information (purchase amount, purchased item, purchase date and time, purchase location information, etc.) when a user purchases a product and transmits the data to a server.

[1077] A "server" is a computer system that receives and stores purchase information sent from a user terminal.

[1078] "Generative AI" is artificial intelligence that analyzes users' purchase information, purchase history, and preference data.

[1079] "Means for predicting benefits and coupons" refers to a function that uses generative AI to select the most suitable benefits and coupons for users.

[1080] "Means of applying to a user's account" refers to the function of registering the benefits and coupons predicted by the generation AI to a user's account.

[1081] "Means of notification" refers to a function that notifies the user device that a benefit or coupon has been applied.

[1082] "Real-time delivery" refers to the ability to instantly provide rewards and coupons to users when they make a purchase in a physical store.

[1083] "Smart devices" refer to electronic devices that can be worn or carried by users, and primarily include smartphones, smart glasses, head-mounted displays, etc.

[1084] The present invention is a system that allows users to easily receive optimal rewards and coupons in real time while shopping in a physical store. This system is implemented using a user terminal, a server, and a generation AI.

[1085] 1. User Device

[1086] The user device is a portable electronic device such as a smartphone or smart glasses. When a user purchases an item in a store, the device acquires purchase information (purchase amount, purchased item, purchase date and time, purchase location information) and sends it to the server. This process is performed in real time, allowing the user to receive optimal benefits and coupons while in the store.

[1087] 2. Server

[1088] The server receives purchase information sent from the user's device and stores it in a database. It also provides the purchase information to the generation AI and requests it to predict the best rewards and coupons. The server may use database software such as SQLite or MySQL. Once the best rewards and coupons are predicted, they are automatically applied to the user's account.

[1089] 3. Generation AI

[1090] The Generative AI analyzes the purchase information provided by the server, as well as the user's past purchase history and preference data. This allows it to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. The Generative AI uses advanced artificial intelligence technologies such as the OpenAI API.

[1091] Specific examples

[1092] For example, if a user makes a purchase of 5,000 yen at a specific physical store, the user's device collects this purchase information. The purchase amount, purchased items, purchase date and time, and purchase location information are acquired and sent to the server. The server receives this information and stores it in a database. The server then passes this stored purchase information to the generation AI and requests it to predict the most appropriate perks and coupons. The generation AI analyzes past purchase history and preference data and predicts that, for example, a "10% off coupon" or a "1,000 yen off coupon" would be optimal. The server applies the perks and coupons to the user's account based on the prediction results from the generation AI and notifies the user's device of the application results.

[1093] Example prompts for generative AI models

[1094] User ID: user123

[1095] Purchase information: Purchase amount 5,000 yen, product ["Product A", "Product B"], date and time 2023-10-01 14:00, store "Store X"

[1096] Past purchase history: [Past purchase data]

[1097] Predict the best coupons.”

[1098] This allows users to receive the most suitable offers and coupons in real time while shopping in a physical store without any hassle, improving the user's purchasing experience and also bringing the benefit of increased sales to the store.

[1099] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1100] Step 1:

[1101] A user purchases a product at a physical store. The user's device (smartphone or smart glasses) collects purchase information in real time. Specifically, it acquires the purchase amount, purchased product, purchase date and time, and purchase location information. The input is the user's detailed purchase information, which the device collects as data.

[1102] Step 2:

[1103] The user terminal sends the collected purchase information to the server. The input is the purchase information collected in step 1, which the terminal prepares as transmission data and sends to the server over the network. The output is the purchase information sent to the server.

[1104] Step 3:

[1105] The server receives the purchase information sent from the user terminal and stores it in a database. The input is the purchase information sent from the user terminal, which the server takes as received data, converts into an appropriate format, and stores in the database. The output is the purchase information stored in the database.

[1106] Step 4:

[1107] The server sends the stored purchase information to the generation AI and requests it to predict the optimal perks and coupons. The input is the purchase information stored in the database, which the server formats for the generation AI and sends. The generation AI performs data analysis based on the input data and predicts the optimal perks and coupons. The output is the predicted perks and coupons.

[1108] Step 5:

[1109] The server applies rewards and coupons to the user's account based on the predictions returned by the generation AI. The input is the predictions returned by the generation AI, which the server processes as data to apply to the user's account. The output is the rewards and coupons applied to the user's account.

[1110] Step 6:

[1111] The server notifies the user terminal of the results of the coupon or reward applied to the user account. The input is the reward or coupon information applied to the user account, which the server prepares as a notification message and sends to the user terminal. The output is the user terminal that received the notification.

[1112] Step 7:

[1113] The user device receives notifications from the server and displays the results of coupon and reward application to the user. The input is the notification message sent from the server, which the device renders to visually present to the user. The output is the notification displayed to the user.

[1114] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1115] The present invention combines a system that allows users to make purchases using optimal benefits and coupons without hassle with an emotion engine that recognizes user emotions. This system is implemented using a user terminal, a server, a generation AI, and an emotion engine.

[1116] 1. User Device

[1117] The user device is responsible for collecting purchase information and sending it to the server. When a user purchases a product, the user device acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.). The acquired purchase information is then sent to the server. The user device also has an interface for recognizing the user's emotions and sends emotional data to the emotion engine.

[1118] 2. Server

[1119] The server receives and stores purchase information and emotion data sent from the user's device. Based on the stored data, the server uses generative AI to predict optimal rewards and coupons. The predicted rewards and coupons are automatically applied to the user's account, and the application result is notified to the user's device.

[1120] 3. Generation AI

[1121] The generation AI analyzes the purchase information provided by the server and the user's purchase history and preference data. It also takes into account the emotional data provided by the emotion engine to predict the best perks and coupons for each user. This prediction is made in real time every time a user makes a purchase. By combining this with the purchase amount and purchase location information, the generation AI selects the most effective coupon under specific conditions and returns it to the server.

[1122] 4. Emotion Engine

[1123] The emotion engine is a system for recognizing user emotions and analyzes emotion data collected from the user's device. The results of the emotion engine's analysis are provided to the generative AI and used to predict rewards and coupons.

[1124] Specific examples

[1125] For example, consider the case where User B makes a purchase of 5,000 yen at a specific shop. User B's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. Emotional data, such as whether the user was happy with the purchase, is also collected at the same time.

[1126] The server receives this information and emotional data and stores the purchase information in a database. The server then passes the stored data to the generation AI, asking it to predict the optimal rewards and coupons. The generation AI analyzes User B's past purchase history, preference data, and emotional data, and predicts that a specific coupon will be optimal. For example, if the user is happy, it might consider an "additional 10% off coupon," or if the user is sad, it might consider a "1,000 yen off coupon."

[1127] The server applies the rewards and coupons to User B's account based on the predictions returned by the generation AI. The server then notifies User B of the application results. User B can confirm that the coupon has been applied and use it the next time they make a purchase.

[1128] In this way, the present invention provides a system that allows users to enjoy shopping without hassle by using optimal benefits and coupons according to their emotional state.

[1129] The processing flow will be explained below.

[1130] Step 1:

[1131] The user completes the purchase. The user's device collects purchase information (purchase amount, purchased product, purchase date and time, and purchase location information).

[1132] Step 2:

[1133] The user's device collects purchase information along with emotional data from the user, which is acquired through an emotion engine based on the user's facial expressions, tone of voice, etc.

[1134] Step 3:

[1135] The user's device sends the collected purchase information and emotion data to the server.

[1136] Step 4:

[1137] The server receives the purchase information and emotion data sent from the user terminal.

[1138] Step 5:

[1139] The server stores the received purchase information and emotion data in a database.

[1140] Step 6:

[1141] The server provides the stored purchase information and emotional data to the generation AI and asks it to predict the best offers and coupons.

[1142] Step 7:

[1143] The generative AI analyzes purchase information, emotional data, and past purchase history and preference data provided by the server.

[1144] Step 8:

[1145] Based on the analysis results, the generative AI predicts the most appropriate rewards and coupons, taking into account the user's current emotional state. For example, if the user is happy, it will select an "additional 10% off coupon," and if they are sad, it will select a "1,000 yen off coupon."

[1146] Step 9:

[1147] The generation AI returns the prediction results to the server.

[1148] Step 10:

[1149] The server applies rewards and coupons to the user's account based on the predictions returned by the generative AI.

[1150] Step 11:

[1151] The server notifies the user terminal of the results of applying the benefits and coupons.

[1152] Step 12:

[1153] The user terminal receives a notification from the server and notifies the user that the coupon has been applied.

[1154] Step 13:

[1155] The user redeems the applied coupon on their next purchase.

[1156] Example 2

[1157] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1158] Conventional coupon and reward systems only consider users' purchase information, and are therefore unable to provide optimal rewards and coupons that reflect the user's emotional state. This can result in a lack of improvement in the user's purchasing experience and a decrease in satisfaction with the system.

[1159] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1160] In this invention, the server includes means for collecting user purchase information and emotional data from the user terminal, means for receiving and storing the purchase information and emotional data, means for predicting optimal benefits and coupons based on the purchase information and emotional data using a generation AI, means for applying the predicted benefits and coupons to the user's account, and means for notifying the user terminal of the coupon application results, thereby making it possible to provide optimal benefits and coupons that take into account the user's emotional state.

[1161] A "user terminal" is a device used by a user when purchasing a product, and is a device that collects purchase information and emotional data and transmits them to a server.

[1162] "Purchase information" refers to information relating to a transaction that occurs when a user purchases a product, and specifically includes data such as the purchase amount, the purchased product, the purchase date and time, and the purchase location.

[1163] "Emotion data" is data that indicates the user's emotional state, and includes information obtained from the user's facial expressions and voice.

[1164] The "server" is a central processing unit that receives and stores data sent from user devices, and then works with the generation AI to predict optimal benefits and coupons and provide them to users.

[1165] "Generative AI" is an artificial intelligence model that analyzes data stored on a server and predicts optimal rewards and coupons based on users' purchase information and emotional data.

[1166] "Special Offers and Coupons" refers to discounts and other preferential treatments that are applied when a user purchases a product, and are incentives to improve the user's purchasing experience.

[1167] The "means for applying predicted rewards and coupons to a user's account" refers to a system that processes the rewards and coupons predicted by the generating AI to associate them with a user's unique digital account.

[1168] "Means for notifying the user terminal of the results of applying a coupon" refers to a communication method for informing the user terminal that a benefit or coupon has been applied to the user's account, and is a system that has a feedback function to the user.

[1169] This invention is a system that helps users make optimal choices when using special offers and coupons without hassle. It is particularly characterized by combining a user's purchase information with emotional data to provide individually optimized special offers and coupons. This system is realized by combining a user terminal, a server, a generation AI, and an emotion engine.

[1170] 1. User Device

[1171] The user terminal collects information when the user makes a purchase and sends it to the server. It also has an interface for recognizing the user's emotions and sends the emotional data to the emotion engine. This terminal is usually implemented as a device such as a smartphone, tablet, or POS system.

[1172] When a user purchases a product, the user's device acquires purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) and sends this information to the server. The device also collects the user's facial expressions and voice, and sends this as emotion data to the emotion engine. For example, if a user purchases 5,000 yen worth of groceries at a supermarket and is smiling, the emotion data of "joy" is sent along with the purchase amount, purchased product, and location information.

[1173] 2. Server

[1174] The server receives the purchase information and emotion data and stores them in a database. Based on this data, the server generates a prompt message that asks the AI ​​to predict rewards and coupons. For example, a prompt message might be created such as, "User B made a purchase worth 5,000 yen and expressed joy. Please predict the best coupon based on this information."

[1175] The server applies the prediction results (rewards and coupons) returned by the generation AI to the user's account and notifies the user of the results, allowing the user to know which coupons are available for their next purchase.

[1176] 3. Generation AI

[1177] The generation AI analyzes purchase information and sentiment data provided by the server to predict the best rewards and coupons for each user. This analysis also takes into account the user's past purchase history and preference data. The generation AI makes predictions in real time and selects the most effective coupons under specific conditions.

[1178] For example, if the user has a history of making purchases from the same store in the past and expressed the emotion of "joy" at that time, the AI ​​will predict that an "additional 10% off coupon" would be optimal. This prediction result is returned to the server, where it is actually applied.

[1179] 4. Emotion Engine

[1180] The emotion engine is software that recognizes user emotions and analyzes emotional data collected from the user's device. The analysis results are provided to the generation AI, which then reflects the emotional data in predicting rewards and coupons, enabling the provision of coupons that are more optimal for the user.

[1181] The emotion engine analyzes the user's emotional state, such as "joy," "sadness," or "anger," and the generative AI then selects the most appropriate rewards and coupons.

[1182] Specific examples

[1183] When User B makes a purchase of 5,000 yen at a specific shop, User B's device collects the purchase amount, purchased items, purchase date and time, and purchase location information, and sends this information to the server. User emotional data is also collected at the same time.

[1184] The server receives this information and emotion data and stores the purchase information in a database. The server then asks the generation AI to predict rewards and coupons based on the generated prompt. The generation AI analyzes User B's past purchase history, preference data, and emotion data to predict which specific coupon is most appropriate. For example, if the user is happy, an "additional 10% off coupon" is considered, and if the user is sad, a "1,000 yen off coupon" is considered.

[1185] The server applies the rewards and coupons to User B's account based on the predictions returned by the generation AI. The server then notifies User B of the application results on his / her device. User B will receive a notification that the coupon has been applied, and can use it the next time he / she makes a purchase.

[1186] The above is an embodiment of the present invention. This system allows users to enjoy shopping without hassle by using the best offers and coupons according to their emotional state.

[1187] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1188] Step 1:

[1189] The user's device collects purchase information.

[1190] When a user purchases a product, the user's device automatically acquires the purchase information (purchase amount, purchased product, purchase date and time, purchase location, etc.). Specifically, this data is collected from POS systems and online purchase screens. The input is the user's purchase action, and the output is the collected purchase information.

[1191] Step 2:

[1192] The user's device collects emotional data.

[1193] The user device uses a built-in camera and microphone to record the user's facial expressions and voice at the time of purchase and analyzes them as emotional data. The input is the user's facial expressions and voice, and the output is the analyzed emotional data.

[1194] Step 3:

[1195] The user terminal transmits the purchase information and emotion data to the server.

[1196] The collected purchase information and emotion data are sent in encrypted form from the user's device to the server. The input is the purchase information and emotion data, and the output is a transmission completion notification to the server.

[1197] Step 4:

[1198] A server stores purchase information and emotion data.

[1199] The server stores the received purchase information and sentiment data in a database, which is important for the generative AI to analyze the data later. The input is the transmitted data, and the output is the stored data.

[1200] Step 5:

[1201] The server generates a prompt message that asks the AI ​​to predict rewards and coupons.

[1202] The server generates a prompt based on the stored data and sends it to the generation AI. For example, the prompt might be something like, "User B made a purchase worth 5,000 yen and expressed happiness. Please predict the best coupon based on this information." The input is the stored data, and the output is the generated prompt.

[1203] Step 6:

[1204] Generative AI analyzes purchase information and sentiment data to predict the best offers and coupons.

[1205] The generative AI reads data from the prompt and predicts rewards and coupons based on the user's past history and preferences. The input is the prompt and related data, and the output is the predicted rewards and coupons.

[1206] Step 7:

[1207] The server applies the generated offers and coupons to the user's account.

[1208] The server applies the coupon to the user's account based on the coupon information returned by the generation AI. The input is the predicted coupon information, and the output is a notification that the coupon has been applied to the user's account.

[1209] Step 8:

[1210] The server notifies the user terminal of the coupon application result.

[1211] The server sends the coupon application result in the form of a notification to the user terminal to inform the user. The input is the coupon application completion notification, and the output is the notification to the user terminal.

[1212] This is the specific processing flow of the program for this system. Through this step, users can receive the best rewards and coupons according to their emotional state.

[1213] (Application example 2)

[1214] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1215] Modern content distribution services face challenges in allowing users to easily receive personalized rewards and coupons. Furthermore, traditional reward and coupon offerings fail to consider the user's emotional state, making it difficult to maximize user satisfaction. Furthermore, there is a demand for more accurate personalization through the integrated use of both viewing data and purchase data. The present invention aims to provide a system that solves these challenges and improves the user experience.

[1216] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting user purchase information and viewing information from the user terminal, means for transmitting the collected purchase information and viewing information to the server, means for the server to receive and store the user purchase information and viewing information, means for predicting optimal benefits and coupons based on the purchase information and viewing information using a generation AI, means for collecting and analyzing user emotion data and applying benefits and coupons based on the emotions, means for applying the predicted benefits and coupons to the user's account, and means for notifying the user terminal of the coupon application results. This makes it possible to provide optimal benefits and coupons that take the user's emotional state into consideration in real time, thereby enhancing personalization for individual users and improving the user experience.

[1217] "User terminal" refers to an electronic device that allows a user to input, view, and operate information, including a smartphone or smart glasses.

[1218] "Purchase information" refers to detailed information about the products purchased by the user, including the purchase amount, the purchased products, the purchase date and time, and the purchase location information.

[1219] "Viewing information" refers to behavioral data of a user when viewing content, and includes the type of content viewed, viewing time, viewing frequency, and the like.

[1220] A "server" is a computing device that receives, stores, and processes data sent by users.

[1221] "Generative AI" is an artificial intelligence system that uses data stored on a server to predict specific outputs, such as optimal rewards or coupons.

[1222] "Emotional data" is data that indicates the emotional state of a user, and is collected using sensors such as a camera and a microphone.

[1223] An "emotion engine" is a system that analyzes collected emotional data and identifies the user's emotional state.

[1224] "Benefits and coupons" are incentives such as discounts and services provided to users, and are applied based on the user's purchasing or viewing behavior.

[1225] An "account" is a user identification database used to manage a user's personal information, behavioral history, benefits, coupons, etc.

[1226] "Notification" is a communication method for conveying information from the system to the user, and is done via a smartphone, smart glasses, etc.

[1227] This invention is a system that collects user viewing and purchasing information from a content distribution service used by the user, and provides optimal benefits and coupons based on this information. This system is implemented by combining a user terminal, a server, a generation AI, and an emotion engine.

[1228] First, the user terminal is an electronic device used by the user, such as a smartphone or smart glasses. When the user watches video or music content, viewing information (type of content watched, viewing time, viewing frequency, etc.) and purchase information (purchase amount, purchased product, purchase date and time, purchase location information, etc.) are collected. In addition, emotion data is collected using the user's emotion recognition function. This information is sent to the server.

[1229] The server receives and stores viewing information, purchase information, and emotional data sent from the user's device. This information is stored in a database on the server. Based on this stored data, the server uses generative AI to predict optimal rewards and coupons. Rewards and coupons are predicted through an integrated analysis of the user's purchase history, preference data, and emotional data.

[1230] Generative AI models such as OpenAI's GPT series are used. This generative AI analyzes viewing information, purchase information, and emotional data to predict the best rewards and coupons for users in real time. For example, incentives are provided based on the user's emotions at the time, such as a "reward for the next viewing being free" when the user is watching an enjoyable video, or a "reward for viewing points" when the user is sad.

[1231] The emotion engine analyzes emotion data acquired from the user's device and identifies the user's emotional state. For example, emotion recognition software such as the Affectiva SDK is used. The emotion data analyzed by the emotion engine is provided to the generative AI.

[1232] When a reward or coupon is predicted, the server applies it to the user's account and sends a notification to the user's device. The user's device then notifies the user of the details of the reward or coupon that has been applied, allowing the user to easily confirm that the reward or coupon has been applied.

[1233] Specific examples

[1234] Suppose a user is using a social networking app on their smartphone. While the user is watching a funny comedy show, the smartphone's camera and microphone detect the emotion "happy / laughing." This information is sent to a server, where a generative AI predicts rewards based on the following prompt:

[1235] Viewing data: {Video title: 'Funny comedy show', Viewing time: '30 minutes'}

[1236] Emotion data: {Happiness: 80%, Laughter: 70%}

[1237] User history: {Past month viewing time: '30 hours', Viewing history: 'Comedy, Action'}

[1238] Please suggest the best offer.

[1239] Based on this prompt, the AI ​​generator predicts the "next viewing free offer" and applies it to the user's account. This offer is then notified to the user's device, allowing them to use it the next time they watch.

[1240] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1241] Step 1:

[1242] The user device begins collecting the user's viewing information and emotional data. Specifically, it uses the camera and microphone installed in the smartphone or smart glasses to collect the user's viewing behavior (how much of what content was viewed) and emotional state (happiness, sadness, excitement, etc.) in real time. This data is then temporarily stored.

[1243] Input: Viewing information (type of content, viewing time, viewing frequency), emotional data (video and audio data from camera and microphone)

[1244] Output: Collected viewing information and emotional data

[1245] Step 2:

[1246] The user device transmits the collected viewing information and emotion data to the server, which receives this data and stores it in a database.

[1247] Input: Collected viewing information and emotional data

[1248] Output: Data saved on the server

[1249] Step 3:

[1250] The server inputs prompts into the generative AI model based on the stored viewing information and emotional data to predict optimal rewards and coupons. The generative AI analyzes viewing information, emotional data, and the user's past viewing history and preference data to generate rewards and coupons.

[1251] Input: Viewing information, emotional data, user's past viewing history and preference data

[1252] Output: Generated offers and coupons

[1253] Step 4:

[1254] The server receives the results from the generative AI and applies the best offers and coupons to the user's account, which are then stored in the user's account database.

[1255] Input: Rewards and coupons from generated AI

[1256] Output: Rewards and coupons applied to user account

[1257] Step 5:

[1258] The server notifies the user device of the applied benefits and coupons. The user device receives this notification and displays the details of the benefits and coupons to the user. The user can check the notification and use these benefits and coupons the next time they watch or purchase.

[1259] Input: Application result to user account

[1260] Output: Notification of special offers and coupons to user devices

[1261] This will enable a system that delivers highly personalized offers and coupons in real time based on the user's emotional state and viewing information.

[1262] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1263] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1264] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1265] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1266] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1267] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1268] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1269] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1270] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1271] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1272] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1273] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1274] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1275] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1276] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1277] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1278] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1279] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1280] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1281] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1282] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1283] The following is further disclosed regarding the above embodiment.

[1284] (Claim 1)

[1285] A means for collecting user purchase information from the user's device;

[1286] means for transmitting the collected purchase information to a server;

[1287] A means for the server to receive and store user purchase information;

[1288] A method to predict optimal rewards and coupons based on purchase information using generative AI,

[1289] A means for applying predicted rewards and coupons to a user's account;

[1290] A means for notifying the user terminal of the coupon application result;

[1291] A system including:

[1292] (Claim 2)

[1293] 2. The system according to claim 1, wherein the purchase information includes the purchase amount, the purchased item, the purchase date and time, and the purchase location information.

[1294] (Claim 3)

[1295] The system of claim 1, including a generation AI that analyzes user purchase history and preference data.

[1296] "Example 1"

[1297] (Claim 1)

[1298] A means for collecting user purchase information from the user's device;

[1299] means for transmitting the collected purchase information to a server;

[1300] A means for the server to receive and store user purchase information;

[1301] A method to predict optimal rewards and coupons based on purchase information using generative AI,

[1302] A means for applying predicted rewards and coupons to a user's account;

[1303] A means for notifying the user terminal of the coupon application result;

[1304] A means for the generative AI to analyze purchase history and preference data in real time,

[1305] means for transmitting purchase information using a secure communications protocol;

[1306] A system including:

[1307] (Claim 2)

[1308] 2. The system according to claim 1, wherein the purchase information includes the purchase amount, the purchased item, the purchase date and time, and the purchase location information.

[1309] (Claim 3)

[1310] The system of claim 1, including a generation AI that analyzes user purchase history and preference data.

[1311] "Application Example 1"

[1312] (Claim 1)

[1313] A means for collecting user purchase information from the user's device;

[1314] means for transmitting the collected purchase information to a server;

[1315] A means for the server to receive and store user purchase information;

[1316] A method to predict optimal rewards and coupons based on purchase information using generative AI,

[1317] A means for applying predicted rewards and coupons to a user's account;

[1318] A means for notifying the user terminal of the coupon application result;

[1319] and a means including smart devices that provide users with the best coupons and offers in real time when making purchases in physical stores.

[1320] A system including:

[1321] (Claim 2)

[1322] 2. The system according to claim 1, wherein the purchase information includes the purchase amount, the purchased item, the purchase date and time, and the purchase location information.

[1323] (Claim 3)

[1324] The system of claim 1, including a generation AI that analyzes user purchase history and preference data.

[1325] "Example 2: Combining Emotion Engines"

[1326] (Claim 1)

[1327] A means for collecting user purchase information from the user's device;

[1328] a means for collecting user emotional data;

[1329] means for transmitting the collected purchase information and emotion data to a server;

[1330] A server receives and stores user purchase information and emotion data;

[1331] A means of using generative AI to predict optimal rewards and coupons based on purchase information and sentiment data;

[1332] A means for applying predicted rewards and coupons to a user's account;

[1333] A means for notifying the user terminal of the coupon application result;

[1334] A system including:

[1335] (Claim 2)

[1336] The system according to claim 1, wherein the purchase information includes the purchase amount, the purchased item, the purchase date and time, and the purchase location information, and the emotional data uses the user's facial expression and voice.

[1337] (Claim 3)

[1338] The system of claim 1, including a generative AI that analyzes a user's purchase history, preference data, and emotional data.

[1339] "Application example 2 when combining emotion engines"

[1340] (Claim 1)

[1341] A means for collecting user purchase information and viewing information from the user terminal;

[1342] means for transmitting the collected purchase information and viewing information to a server;

[1343] A means for the server to receive and store user purchase information and viewing information;

[1344] A method for predicting optimal rewards and coupons based on purchase and viewing information using generative AI;

[1345] A means to collect and analyze user sentiment data and apply rewards and coupons based on sentiment;

[1346] A means for applying predicted rewards and coupons to a user's account;

[1347] A means for notifying the user terminal of the coupon application result;

[1348] A system including:

[1349] (Claim 2)

[1350] 2. The system according to claim 1, wherein the purchase information includes the purchase amount, the purchased item, the purchase date and time, and the purchase location information, and the viewing information includes the type of viewing content, the viewing time, and the viewing frequency.

[1351] (Claim 3)

[1352] 10. The system of claim 1, including a generative AI that analyzes a user's purchase history, preference data, and emotional data. [Explanation of symbols]

[1353] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for collecting user purchase information from the user's device; means for transmitting the collected purchase information to a server; A means for the server to receive and store user purchase information; A method to predict optimal rewards and coupons based on purchase information using generative AI, A means for applying predicted rewards and coupons to a user's account; A means for notifying the user terminal of the coupon application result; A system including:

2. 2. The system according to claim 1, wherein the purchase information includes purchase amount, purchased item, purchase date and time, and purchase location information.

3. The system of claim 1, further comprising a generation AI that analyzes a user's purchase history and preference data.

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